Search results for: flood forecast

Authors: Ubaidillah Zuhdi, Shunsuke Mori, Kazuhisa Kamegai

Abstract:

The purpose of this study is to forecast the influences of Information and Communication Technology (ICT) on the structural changes of Japanese economies based on Leontief Input-Output (IO) coefficients. This study establishes a statistical analysis to predict the future interrelationships among industries. We employ the Constrained Multivariate Regression (CMR) model to analyze the historical changes of input-output coefficients. Statistical significance of the model is then tested by Likelihood Ratio Test (LRT). In our model, ICT is represented by two explanatory variables, i.e. computers (including main parts and accessories) and telecommunications equipment. A previous study, which analyzed the influences of these variables on the structural changes of Japanese industrial sectors from 1985-2005, concluded that these variables had significant influences on the changes in the business circumstances of Japanese commerce, business services and office supplies, and personal services sectors. The projected future Japanese economic structure based on the above forecast generates the differentiated direct and indirect outcomes of ICT penetration.

Keywords: forecast, ICT, industrial structural changes, statistical analysis

Procedia PDF Downloads 348

Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

Abstract:

The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: wind power, Gaussien process, modelling, forecasting

Procedia PDF Downloads 374

Authors: Qamar Iqbal

Abstract:

Companies strive to improve their forecasting methods to predict the fluctuations in customer demand. These fluctuation and variation in demand affect the manufacturing operations and can limit a company’s ability to fulfill customer demand on time. Companies keep the inventory buffer and maintain the stocking levels to reduce the impact of demand variation. A mid-size company deals with thousands of stock keeping units (skus). It is neither easy and nor efficient to control and manage each sku. Inventory classification provides a tool to the management to increase their ability to support customer demand. The paper presents a framework that shows how inventory classification can play a role to increase supply chain responsiveness. A case study will be presented to further elaborate the method both for build-to-order and build-to-forecast manufacturing environments. Results will be compared that will show which manufacturing setting has advantage over another under different circumstances. The outcome of this study is very useful to the management because this will give them an insight on how inventory classification can be used to increase their ability to respond to changing customer needs.

Keywords: inventory classification, supply chain responsiveness, forecast, manufacturing environment

Procedia PDF Downloads 571

Authors: Cheong Hyeon Oh, Dongho Nam, Byungsik Kim

Abstract:

Due to the recent climate change, flood damage caused by local floods and typhoons has frequently occurred, the incidence rate and intensity of wildfires are greatly increased due to increased temperatures and changes in precipitation patterns. Wildfires cause primary damage, such as loss of forest resources, as well as secondary disasters, such as landslides, floods, and debris flow. In many countries around the world, damage and economic losses from secondary damage are occurring as well as the direct effects of forest fires. Therefore, in this study, the Rainfall-Runoff model(S-RAT) was used for the wildfire affected areas in Gangneung and Goseong, which occurred on April 2019, when the stability of vegetation and soil were destroyed by wildfires. Rainfall data from Typhoon Rusa were used in the S-RAT model, and flood discharge was calculated according to changes in land cover before and after wildfire damage. The results of the calculation showed that flood discharge increased significantly due to changes in land cover, as the increase in flood discharge increases the possibility of the occurrence of the debris flow and the extent of the damage, the debris flow height and range were calculated before and after forest fire using RAMMS. The analysis results showed that the height and extent of damage increased after wildfire, but the result value was underestimated due to the characteristics that using DEM and maximum flood discharge of the RAMMS model. This research was supported by a grant(2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS). This paper work (or document) was financially supported by Ministry of the Interior and Safety as 'Human resoure development Project in Disaster management'.

Keywords: wildfire, debris flow, land cover, rainfall-runoff meodel S-RAT, RAMMS, height

Procedia PDF Downloads 93

Authors: Karim Ahmed

Abstract:

The purpose of this study was to investigate how factoring the impact of natural disasters beyond flooding would affect managed retreat policy eligibility in the United States. For the study design, a correlation analysis method compared weighted measures of flooding and other natural disasters (e.g., wildfires, tornadoes, heatwaves, etc.) to CBSA Populated areas, the prevalence of cropland, and relative poverty on a county level. The study found that the vast majority of CBSAs eligible for managed retreat programs under a policy inclusive of non-flooding events would have already been covered by flood-only managed retreat policies. However, it is noteworthy that a majority of those counties that are not covered by a flood-only managed retreat policy have high rates of poverty and are either heavily populated and/or agriculturally active. The correlation is particularly strong between counties that are subject to multiple natural hazards and those that have both high rates of relative poverty and cropland prevalence. There is currently no managed retreat policy for agricultural land in the United States despite the environmental implications and food supply chain vulnerabilities related to at-risk cropland. The findings of this study suggest both that such a policy should be created and, when it is, that special attention should be paid to non-flood natural disasters affecting agricultural areas. These findings also reveal that, while current flood-based policies in the United States serve many areas that do need access to managed retreat funding and implementation, other vulnerable areas are overlooked by this approach. These areas are often deeply impoverished and are therefore particularly vulnerable to natural disaster; if and when those disasters do occur, these areas are often less financially prepared to recover or retreat from the disaster’s advance and, due to the limitations of the current policies discussed above, are less able to take the precautionary measures necessary to mitigate their risk.

Keywords: flood, hazard, land use, managed retreat, wildfire

Procedia PDF Downloads 99

Authors: Md. Abud Darda

Abstract:

Different options for natural gas production in wide geographic areas may be described through diffusion of innovation models. This type of modeling approach provides an indirect estimate of an ultimately recoverable resource, URR, capture the quantitative effects of observed strategic interventions, and allow ex-ante assessments of future scenarios over time. In order to ensure a sustainable energy policy, it is important to forecast the availability of this natural resource. Considering a finite life cycle, in this paper we try to investigate the natural gas production of Myanmar and Algeria, two important natural gas provider in the world energy market. A number of homogeneous and heterogeneous diffusion models, with convenient extensions, have been used. Models validation has also been performed in terms of prediction capability.

Keywords: diffusion models, energy forecast, natural gas, nonlinear production

Procedia PDF Downloads 202

Authors: Jaturong Som-ard

Abstract:

The heavy rainfall from 3rd to 22th January 2017 had swamped much area of Ranot district in southern Thailand. Due to heavy rainfall, the district was flooded which had a lot of effects on economy and social loss. The major objective of this study is to detect flooding extent using Sentinel-1A data and identify a number of damaged buildings over there. The data were collected in two stages as pre-flooding and during flood event. Calibration, speckle filtering, geometric correction, and histogram thresholding were performed with the data, based on intensity spectral values to classify thematic maps. The maps were used to identify flooding extent using change detection, along with the buildings digitized and collected on JOSM desktop. The numbers of damaged buildings were counted within the flooding extent with respect to building data. The total flooded areas were observed as 181.45 sq.km. These areas were mostly occurred at Ban khao, Ranot, Takhria, and Phang Yang sub-districts, respectively. The Ban khao sub-district had more occurrence than the others because this area is located at lower altitude and close to Thale Noi and Thale Luang lakes than others. The numbers of damaged buildings were high in Khlong Daen (726 features), Tha Bon (645 features), and Ranot sub-district (604 features), respectively. The final flood extent map might be very useful for the plan, prevention and management of flood occurrence area. The map of building damage can be used for the quick response, recovery and mitigation to the affected areas for different concern organization.

Keywords: flooding extent, Sentinel-1A data, JOSM desktop, damaged buildings

Procedia PDF Downloads 162

Authors: Alireza Abbassi Monjezi, Mohammad Hasan Shaheed

Abstract:

Flood, caused by excessive rainfall, monsoon, cyclone and tsunami is a common disaster in many countries of the world especially sea connected low-lying countries. A stand-alone self-powered water filtration module for decontamination of floodwater has been designed and modeled. A combination forward osmosis – low pressure reverse osmosis (FO-LPRO) system powered by solar photovoltaic-thermal (PVT) energy is investigated which could overcome the main barriers to water supply for remote areas and ensure off-grid filtration. The proposed system is designed to be small scale and portable to provide on-site potable water to communities that are no longer themselves mobile nor can be reached quickly by the aid agencies. FO is an osmotically driven process that uses osmotic pressure gradients to drive water across a controlled pore membrane from a feed solution (low osmotic pressure) to a draw solution (high osmotic pressure). This drops the demand for high hydraulic pressures and therefore the energy demand. There is also a tendency for lower fouling, easier fouling layer removal and higher water recovery. In addition, the efficiency of the PVT unit will be maximized through freshwater cooling which is integrated into the system. A filtration module with the capacity of 5 m3/day is modeled to treat floodwater and provide drinking water. The module can be used as a tool for disaster relief, particularly in the aftermath of flood and tsunami events.

Keywords: flood resilience, membrane desalination, portable water treatment, solar energy

Procedia PDF Downloads 263

Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

Procedia PDF Downloads 163

Authors: Sitarrine Thongpussawal, Dasarath Jayasuriya, Thanaroj Woraratprasert, Sakawtree Prajamwong

Abstract:

Thailand grapples with recurrent floods causing substantial repercussions on its economy, society, and environment. In 2021, the economic toll of these floods amounted to an estimated 53,282 million baht, primarily impacting the agricultural sector. The existing flood monitoring system in Thailand suffers from inaccuracies and insufficient information, resulting in delayed warnings and ineffective communication to the public. The Office of the National Water Resources (OWNR) is tasked with developing and integrating data and information systems for efficient water resources management, yet faces challenges in monitoring accuracy, forecasting, and timely warnings. This study endeavors to evaluate the viability of enhancing Thailand's Flood Forecasting and Warning (FFW) systems. Additionally, it aims to formulate a comprehensive work package grounded in international best practices to enhance the country's FFW systems. Employing qualitative research methodologies, the study conducted in-depth interviews and focus groups with pertinent agencies. Data analysis involved techniques like note-taking and document analysis. The study substantiates the feasibility of developing and enhancing FFW systems in Thailand. Implementation of international best practices can augment the precision of flood forecasting and warning systems, empowering local agencies and residents in high-risk areas to prepare proactively, thereby minimizing the adverse impact of floods on lives and property. This research underscores that Thailand can feasibly advance its FFW systems by adopting international best practices, enhancing accuracy, and improving preparedness. Consequently, the study enriches the theoretical understanding of flood forecasting and warning systems and furnishes valuable recommendations for their enhancement in Thailand.

Keywords: flooding, forecasting, warning, monitoring, communication, Thailand

Procedia PDF Downloads 34

Authors: Syazreen N. Shair, Saiful A. Ishak, Aida Y. Yusof, Azizah Murad

Abstract:

In this paper, we forecast age-specific Malaysian mortality rates and life expectancy at births by gender and ethnic groups including Malay, Chinese and Indian. Two mortality forecasting models are adopted the original Lee-Carter model and its recent modified version, the product ratio coherent model. While the first forecasts the mortality rates for each subpopulation independently, the latter accounts for the relationship between sub-populations. The evaluation of both models is performed using the out-of-sample forecast errors which are mean absolute percentage errors (MAPE) for mortality rates and mean forecast errors (MFE) for life expectancy at births. The best model is then used to perform the long-term forecasts up to the year 2030, the year when Malaysia is expected to become an aged nation. Results suggest that in terms of overall accuracy, the product ratio model performs better than the original Lee-Carter model. The association of lower mortality group (Chinese) in the subpopulation model can improve the forecasts of high mortality groups (Malay and Indian).

Keywords: coherent forecasts, life expectancy at births, Lee-Carter model, product-ratio model, mortality rates

Procedia PDF Downloads 191

Authors: Nop Sopipan

Abstract:

In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term.

Keywords: volatility, Markov Regime Switching, forecasting, Baht/USD

Procedia PDF Downloads 266

Authors: Vasileiou Apostolos, Theodosiou Chrysa, Tsitroulis Ioannis, Maris Fotios

Abstract:

The severity of extreme phenomena is due to their ability to cause severe damage in a small amount of time. It has been observed that floods affect the greatest number of people and induce the biggest damage when compared to the total of annual natural disasters. The detection of potential flood-prone areas constitutes one of the fundamental components of the European Natural Disaster Management Policy, directly connected to the European Directive 2007/60. The aim of the present paper is to develop a new methodology that combines geographical information, fuzzy logic and multi-criteria evaluation methods so that the most vulnerable areas are defined. Therefore, ten factors related to geophysical, morphological, climatological/meteorological and hydrological characteristics of the basin were selected. Afterwards, two models were created to detect the areas pronest to flooding. The first model defined the gravitas of each factor using Analytical Hierarchy Process (AHP) and the final map of possible flood spots were created using GIS and Boolean Algebra. The second model made use of the fuzzy logic and GIS combination and a respective map was created. The application area of the aforementioned methodologies was in Ardas basin due to the frequent and important floods that have taken place these last years. Then, the results were compared to the already observed floods. The result analysis shows that both models can detect with great precision possible flood spots. As the fuzzy logic model is less time-consuming, it is considered the ideal model to apply to other areas. The said results are capable of contributing to the delineation of high risk areas and to the creation of successful management plans dealing with floods.

Keywords: analytical hierarchy process, flood prone areas, fuzzy logic, geographic information system

Procedia PDF Downloads 346

Authors: Nien-Ming Hong, Kuei-Fang Huang

Abstract:

Climate change is an important issue in future, which seriously affects water resources for a long term planning and management. Flood assessment is highly related with climate and land use. Increasing rainfall and urbanization will induce the inundated area in future. For adapting the impacts of climate change, a land use planning is a good strategy for reducing flood damage. The study is to build a trade-off process with different land use types. The Ta-Liao watershed is the study area with three types of land uses that are build-up, farm and forest. The build-up area is concentrated in the downstream of the watershed. Different rainfall amounts are applied for assessing the land use in 1996, 2005 and 2013. The adapted strategies are based on retarding the development of urban and a trade-off process. When a land changes from farm area to built-up area in downstream, this study is to search for a farm area and change it to forest/grass area or building a retention area in the upstream. For assessing the effects of the strategy, the inundation area is simulated by the Flo-2D model with different rainfall conditions and land uses. The results show inundation maps of several cases with land use change planning. The results also show the trade-off strategies and retention areas can decrease the inundated area and divide the inundated area, which are better than retarding urban development. The land use change is usually non-reverse and the planning should be constructed before the climate change.

Keywords: climate change, land use change, flood risk assessment, land use planning

Procedia PDF Downloads 310

Authors: Betty Makena, Tsegaye Tadesse, Mark Svoboda

Abstract:

Global warming has intensified the frequency and intensity of extreme climate disasters and led to unpredictable weather conditions. Consequently, rapid shifts between drought and floods, known as Drought-Flood Abrupt Alteration (DFAA), have become increasingly common. DFAA results in superimposed impacts of drought and floods within a short period, exacerbating the effects of the floods or drought event. Current disaster management plans often overlook DFAA events, as they primarily focus on either floods or drought. Therefore, effectively identifying DFAA events is crucial for developing effective mitigation strategies. This study aims to identify historical DFAA events in Kenya using the Long Cycle Drought-Flood Abrupt Alteration Index (LDFAI). The research will analyze the spatial distribution, temporal variation, and intensity of DFAA events from 1990 to 2023, as well as their socio-economic impacts. Understanding these events is important to develop more effective strategies to address the impacts of DFAA events. Findings from this study will inform decision making to develop coping strategies to mitigate the adverse effects of DFAA in Kenya.

Keywords: abrupt, alteration, drought, floods

Procedia PDF Downloads 42

Authors: Susanna Pho

Abstract:

With the rise in frequency of high impact flooding events and continued growth across the state, communities in Florida are increasingly turning to innovative higher regulatory standards to keep residents safe. Using models and maps that take future conditions into account can help coastal communities at high risk of flooding adapt, but the associated responsibilities of enforcing and educating stakeholders about floodplain management can be a barrier to adoption. This is especially true in communities that are already managing multiple maps, as adding more regulatory mapping may overwhelm resource-constrained building departments. Without effective workflow solutions, even small tasks like communicating regulations to residents or determining the most restrictive mapping for a permit application can become challenging when dealing with complex maps. Using the case study of Forerunner’s work with a partner community, this session will outline how the community combines technology with robust outreach strategies to boost resident communication. We’ll discuss how software like Forerunner can help communities pull together disparate datasets and mobilize information for property-specific outreach to provide smarter resources to a variety of stakeholders. it’ll explore how incorporating digital workflows into outreach can ensure faster response time, better compliance enforcement, and stronger data continuity. The presentation will also include suggestions on how other communities might be able to leverage data to strengthen their floodplain management programs.

Keywords: floodplain management, flood response, flood risk adaptation, flood protection - products and processes, flood risk assessment and recovery, innovative approaches

Procedia PDF Downloads 32

Authors: Asawari Ajay Avhad

Abstract:

The Soil and Water Assessment Tool (SWAT) is a hydrological model designed to predict the complex interactions within natural and human-altered watersheds. This research applies the SWAT model to the Ulhas River basin, a small watershed undergoing urbanization and characterized by bowl-like topography. Three simulation scenarios (LC17, LC22, and LC27) are investigated, each representing different land use and land cover (LULC) configurations, to assess the impact of urbanization on runoff. The LULC for the year 2027 is generated using the MOLUSCE Plugin of QGIS, incorporating various spatial factors such as DEM, Distance from Road, Distance from River, Slope, and distance from settlements. Future climate data is simulated within the SWAT model using historical data spanning 30 years. A susceptibility map for runoff across the basin is created, classifying runoff into five susceptibility levels ranging from very low to very high. Sub-basins corresponding to major urban settlements are identified as highly susceptible to runoff. With consideration of future climate projections, a slight increase in runoff is forecasted. The reliability of the methodology was validated through the identification of sub-basins known for experiencing severe flood events, which were determined to be highly susceptible to runoff. The susceptibility map successfully pinpointed these sub-basins with a track record of extreme flood occurrences, thus reinforcing the credibility of the assessment methodology. This study suggests that the methodology employed could serve as a valuable tool in flood management planning.

Keywords: future land use impact, flood management, run off prediction, ArcSWAT

Procedia PDF Downloads 14

Authors: C. Li, G. Coates, N. Johnson, M. Mc Guinness

Abstract:

In the UK, flooding is responsible for significant losses to the economy due to the impact on businesses, the vast majority of which are Small and Medium Enterprises (SMEs). Businesses of this nature tend to lack formal plans to aid their response to and recovery from disruptive events such as flooding. This paper reports on work on how an agent-based model (ABM) is being developed based on interview data gathered from SMEs at-risk of flooding and/or have direct experience of flooding. The ABM will enable simulations to be performed allowing investigations of different response strategies which SMEs may employ to lessen the impact of flooding, thus strengthening their resilience.

Keywords: ABM, flood response, SMEs, business continuity

Procedia PDF Downloads 281

Authors: M. Mulumpwa, W. W. L. Jere, M. Lazaro, A. H. N. Mtethiwa

Abstract:

The study considered Seasonal Autoregressive Integrated Moving Average (SARIMA) processes to select an appropriate stochastic model to forecast the monthly data from the Lake Malawi water levels for the period 1986 through 2015. The appropriate model was chosen based on SARIMA (p, d, q) (P, D, Q)S. The Autocorrelation function (ACF), Partial autocorrelation (PACF), Akaike Information Criteria (AIC), Bayesian Information Criterion (BIC), Box–Ljung statistics, correlogram and distribution of residual errors were estimated. The SARIMA (1, 1, 0) (1, 1, 1)12 was selected to forecast the monthly data of the Lake Malawi water levels from August, 2015 to December, 2021. The plotted time series showed that the Lake Malawi water levels are decreasing since 2010 to date but not as much as was the case in 1995 through 1997. The future forecast of the Lake Malawi water levels until 2021 showed a mean of 474.47 m ranging from 473.93 to 475.02 meters with a confidence interval of 80% and 90% against registered mean of 473.398 m in 1997 and 475.475 m in 1989 which was the lowest and highest water levels in the lake respectively since 1986. The forecast also showed that the water levels of Lake Malawi will drop by 0.57 meters as compared to the mean water levels recorded in the previous years. These results suggest that the Lake Malawi water level may not likely go lower than that recorded in 1997. Therefore, utilisation and management of water-related activities and programs among others on the lake should provide room for such scenarios. The findings suggest a need to manage the Lake Malawi jointly and prudently with other stakeholders starting from the catchment area. This will reduce impacts of anthropogenic activities on the lake’s water quality, water level, aquatic and adjacent terrestrial ecosystems thereby ensuring its resilience to climate change impacts.

Keywords: forecasting, Lake Malawi, water levels, water level fluctuation, climate change, anthropogenic activities

Procedia PDF Downloads 197

Authors: Korpapa Srisamai, Pawee Siriruk

Abstract:

The purpose of this study is to forecast product demands and develop appropriate and adequate procurement plans to meet customer needs and reduce costs. When the product exceeds customer demands or does not move, it requires the company to support insufficient storage spaces. Moreover, some items, when stored for a long period of time, cause deterioration to dead stock. A case study of the wholesale company of electronic equipment and components, which has uncertain customer demands, is considered. The actual purchasing orders of customers are not equal to the forecast provided by the customers. In some cases, customers have higher product demands, resulting in the product being insufficient to meet the customer's needs. However, some customers have lower demands for products than estimates, causing insufficient storage spaces and dead stock. This study aims to reduce the loss of sales opportunities and the number of remaining goods in the warehouse, citing 30 product samples of the company's most popular products. The data were collected during the duration of the study from January to October 2022. The methods used to forecast are simple moving averages, weighted moving average, and exponential smoothing methods. The economic ordering quantity and reorder point are used to calculate to meet customer needs and track results. The research results are very beneficial to the company. The company can reduce the loss of sales opportunities by 20% so that the company has enough products to meet customer needs and can reduce unused products by up to 10% dead stock. This enables the company to order products more accurately, increasing profits and storage space.

Keywords: demand forecast, reorder point, lost sale, dead stock

Procedia PDF Downloads 86

Authors: Liang Weichien

Abstract:

Without adequate information and mitigation plan for natural disaster, the risk to urban populated areas will increase in the future as populations grow, especially in Taiwan. Taiwan is recognized as the world's high-risk areas, where an average of 5.7 times of floods occur per year should seek to strengthen coherence and consensus in how cities can plan for flood and climate change. Therefore, this study aims at understanding the vulnerability to flooding in Taipei city, Taiwan, by creating indicators and calculating the vulnerability of each study units. The indicators were grouped into sensitivity and adaptive capacity based on the definition of vulnerability of Intergovernmental Panel on Climate Change. The indicators were weighted by using Principal Component Analysis. However, current researches were based on the assumption that the composition and influence of the indicators were the same in different areas. This disregarded spatial correlation that might result in inaccurate explanation on local vulnerability. The study used Geographically Weighted Principal Component Analysis by adding geographic weighting matrix as weighting to get the different main flood impact characteristic in different areas. Cross Validation Method and Akaike Information Criterion were used to decide bandwidth and Gaussian Pattern as the bandwidth weight scheme. The ultimate outcome can be used for the reduction of damage potential by integrating the outputs into local mitigation plan and urban planning.

Keywords: flood vulnerability, geographically weighted principal components analysis, GWPCA, highly urbanized area, spatial correlation

Procedia PDF Downloads 266

Authors: Liu Fangxin, Zhao Jijun

Abstract:

Nowadays, China and the world are facing the same problems of flooding, city waterlogging and other environment issues. Throughout history, China had many excellent experiences dealing with the flood, and can be used as a significant reference for contemporary urban construction. In view of this, the research used the method of literature analysis to find out the main water storage measures in ancient cities, including reservoir storage and pond water storage. And it used the case study method to introduce the historical evolution, engineering measures and landscape design of 4 typical ancient Chinese cities in details. Then we found the pond and the reservoir were the main infrastructures for the ancient Chinese city to avoid the waterlogging and flood. At last this paper summed up the historical experience of Chinese traditional water storage and made conclusions that the establishment of a reasonable green water storage facilities could be used to solve today's rain and flood problems, and hoped to give some enlightenment of stormwater management to our modern city.

Keywords: ancient Chinese cities, water storage project, Chinese classical gardening, stormwater management, green facilities

Procedia PDF Downloads 314

Authors: Zulkifli Yusop

Abstract:

The severity of December 2014 flood on the east coast of Peninsular Malaysia has raised concern over the adequacy of existing land use practices and policies. This article assesses flood responses to selective logging, plantation establishment (oil palm and rubber) and their subsequent management regimes. The hydrological impacts were evaluated on two levels: on-site (mostly in the upstream) and off-site to reflect the cumulative impact at downstream. Results of experimental catchment studies suggest that on-site impact of flood could be kept to a minimum when selecting logging strictly adhere to the existing guidelines. However, increases in flood potential and sedimentation rate were observed with logging intensity and slope steepness. Forest conversion to plantation show the highest impacts. Except on the heavily compacted surfaces, the ground revegetation is usually rapid within two years upon the cessation of the logging operation. The hydrological impacts of plantation opening and replanting could be significantly reduced once the cover crop has fully established which normally takes between three to six months after sowing. However, as oil palms become taller and the canopy gets closer, the cover crop tends to die off due to light competition, and its protecting function gradually diminishes. The exposed soil is further compacted by harvesting machinery which subsequently leads to greater overland flow and erosion rates. As such, the hydrological properties of matured oil palm plantations are generally poorer than in young plantation. In hilly area, the undergrowth in rubber plantation is usually denser compared to under oil palm. The soil under rubber trees is also less compacted as latex collection is done manually. By considering the cumulative effects of land-use over space and time, selective logging seems to pose the least impact on flood potential, followed by planting rubber for latex, oil palm and Latex Timber Clone (LTC). The cumulative hydrological impact of LTC plantation is the most severe because of its shortest replanting rotation (12 to 15 years) compared to oil palm (25 years) and rubber for latex (35 years). Furthermore, the areas gazetted for LTC are mostly located on steeper slopes which are more susceptible to landslide and erosion. Forest has limited capability to store excess rainfall and is only effective in attenuating regular floods. Once the hydrologic storage is exceeded, the excess rainfall will appear as flood water. Therefore, for big floods, rainfall regime has a much bigger influence than land use.

Keywords: selective logging, plantation, extreme rainfall, debris flow

Procedia PDF Downloads 319

Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

Abstract:

The U.S. Federal Emergency Management Agency (FEMA) developed flood maps based on experts’ experience and estimates of the probability of flooding. Current flood-risk models evaluate flood risk with regional and subjective measures without impact from torrential rain and nuisance flooding at the neighborhood level. Nuisance flooding occurs in small areas in the community, where a few streets or blocks are routinely impacted. This type of flooding event occurs when torrential rainstorm combined with high tide and sea level rise temporarily exceeds a given threshold. In South Florida, this threshold is 1.7 ft above Mean Higher High Water (MHHW). The National Weather Service defines torrential rain as rain deposition at a rate greater than 0.3-inches per hour or three inches in a single day. Data from the Florida Climate Center, 1970 to 2020, shows 371 events with more than 3-inches of rain in a day in 612 months. The purpose of this research is to develop a data-driven method to determine comprehensive analytical damage-avoidance criteria that account for nuisance flood events at the single-family home level. The method developed uses the Failure Mode and Effect Analysis (FMEA) method from the American Society of Quality (ASQ) to estimate the Damage Avoidance (DA) preparation for a 1-day 100-year storm. The Consequence of Nuisance Flooding (CoNF) is estimated from community mitigation efforts to prevent nuisance flooding damage. The Probability of Nuisance Flooding (PoNF) is derived from the frequency and duration of torrential rainfall causing delays and community disruptions to daily transportation, human illnesses, and property damage. Urbanization and population changes are related to the U.S. Census Bureau's annual population estimates. Data collected by the United States Department of Agriculture (USDA) Natural Resources Conservation Service’s National Resources Inventory (NRI) and locally by the South Florida Water Management District (SFWMD) track the development and land use/land cover changes with time. The intent is to include temporal trends in population density growth and the impact on land development. Results from this investigation provide the risk of nuisance flooding as a function of CoNF and PoNF for coastal areas of South Florida. The data-based criterion provides awareness to local municipalities on their flood-risk assessment and gives insight into flood management actions and watershed development.

Keywords: flood risk, nuisance flooding, urban flooding, FMEA

Procedia PDF Downloads 64

Authors: Abhisek Chakrabarty, Subhraprakash Mandal

Abstract:

Flash flood is one of the catastrophic natural hazards in the mountainous region of India. The recent flood in the Mandakini River in Kedarnath (14-17th June, 2013) is a classic example of flash floods that devastated Uttarakhand by killing thousands of people.The disaster was an integrated effect of high intensityrainfall, sudden breach of Chorabari Lake and very steep topography. Every year in Himalayan Region flash flood occur due to intense rainfall over a short period of time, cloud burst, glacial lake outburst and collapse of artificial check dam that cause high flow of river water. In Sikkim-Derjeeling Himalaya one of the probable flash flood occurrence zone is Teesta Watershed. The Teesta River is a right tributary of the Brahmaputra with draining mountain area of approximately 8600 Sq. km. It originates in the Pauhunri massif (7127 m). The total length of the mountain section of the river amounts to 182 km. The Teesta is characterized by a complex hydrological regime. The river is fed not only by precipitation, but also by melting glaciers and snow as well as groundwater. The present study describes an attempt to model surface runoff in upper Teesta basin, which is directly related to catastrophic flood events, by creating a system based on GIS technology. The main object was to construct a direct unit hydrograph for an excess rainfall by estimating the stream flow response at the outlet of a watershed. Specifically, the methodology was based on the creation of a spatial database in GIS environment and on data editing. Moreover, rainfall time-series data collected from Indian Meteorological Department and they were processed in order to calculate flow time and the runoff volume. Apart from the meteorological data, background data such as topography, drainage network, land cover and geological data were also collected. Clipping the watershed from the entire area and the streamline generation for Teesta watershed were done and cross-sectional profiles plotted across the river at various locations from Aster DEM data using the ERDAS IMAGINE 9.0 and Arc GIS 10.0 software. The analysis of different hydraulic model to detect flash flood probability ware done using HEC-RAS, Flow-2D, HEC-HMS Software, which were of great importance in order to achieve the final result. With an input rainfall intensity above 400 mm per day for three days the flood runoff simulation models shows outbursts of lakes and check dam individually or in combination with run-off causing severe damage to the downstream settlements. Model output shows that 313 Sq. km area were found to be most vulnerable to flash flood includes Melli, Jourthang, Chungthang, and Lachung and 655sq. km. as moderately vulnerable includes Rangpo,Yathang, Dambung,Bardang, Singtam, Teesta Bazarand Thangu Valley. The model was validated by inserting the rain fall data of a flood event took place in August 1968, and 78% of the actual area flooded reflected in the output of the model. Lastly preventive and curative measures were suggested to reduce the losses by probable flash flood event.

Keywords: flash flood, GIS, runoff, simulation model, Teesta river basin

Procedia PDF Downloads 272

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

Abstract:

The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 116

Authors: Antonis Skouloudis, Georgios Deligiannakis, Panagiotis Vouros, Konstantinos Evangelinos, Loannis Nikolaou

Abstract:

On November 15th, 2017, a catastrophic flash flood devastated the city of Mandra in Central Greece, resulting in 24 fatalities and extensive damages to the built environment and infrastructure. It was Greece's deadliest and most destructive flood event for the past 40 years. In this paper, we examine the consequences of this event too small and medium-sized enterprises (SMEs) operating in Mandra during the flood event, which were affected by the floodwaters to varying extents. In this context, we conducted semi-structured interviews with business owners-managers of 45 SMEs located in flood inundated areas and are still active nowadays, based on an interview guide that spanned 27 topics. The topics pertained to the disaster experience of the business and business owners-managers, knowledge and attitudes towards climate change and extreme weather, aspects of disaster preparedness and related assistance needs. Our findings reveal that the vast majority of the affected businesses experienced heavy damages in equipment and infrastructure or total destruction, which resulted in business interruption from several weeks up to several months. Assistance from relatives or friends helped for the damage repairs and business recovery, while state compensations were deemed insufficient compared to the extent of the damages. Most interviewees pinpoint flooding as one of the most critical risks, and many connect it with the climate crisis. However, they are either not willing or unable to apply property-level prevention measures in their businesses due to cost considerations or complex and cumbersome bureaucratic processes. In all cases, the business owners are fully aware of the flood hazard implications, and since the recovery from the event, they have engaged in basic mitigation measures and contingency plans in case of future flood events. Such plans include insurance contracts whenever possible (as the vast majority of the affected SMEs were uninsured at the time of the 2017 event) as well as simple relocations of critical equipment within their property. The study offers fruitful insights on latent drivers and barriers of SMEs' resilience capacity to flash flooding. In this respect, findings such as ours, highlighting tensions that underpin behavioral responses and experiences, can feed into a) bottom-up approaches for devising actionable and practical guidelines, manuals and/or standards on business preparedness to flooding, and, ultimately, b) policy-making for an enabling environment towards a flood-resilient SME sector.

Keywords: flash flood, small and medium-sized enterprises, organizational resilience capacity, disaster preparedness, qualitative study

Procedia PDF Downloads 111

Authors: Serkan Aras, Emrah Gulay

Abstract:

Combining various forecasting models is an important tool for researchers to attain more accurate forecasts. A great number of papers have shown that selecting single models as dissimilar models, or methods based on different information as possible leads to better forecasting performances. However, there is not a certain rule regarding the number of single models to be used in any combining methods. This study focuses on determining the optimal or near optimal number for single models with the help of statistical tests. An extensive experiment is carried out by utilizing some well-known time series data sets from diverse fields. Furthermore, many rival forecasting methods and some of the commonly used combining methods are employed. The obtained results indicate that some statistically significant performance differences can be found regarding the number of the single models in the combining methods under investigation.

Keywords: combined forecast, forecasting, M-competition, time series

Procedia PDF Downloads 328

Authors: Etienne Alain Feukeu, L. W. Snyman

Abstract:

Water scarcity is becoming a real issue nowadays. Most countries in the world are facing it in their own way based on their own geographical coordinate and condition. Many countries are facing a challenge of a growing water demand as a result of not only an increased population, economic growth, but also as a pressure of the population dynamic and urbanization. In view to mitigate some of this related problem, an accurate method of water estimation and future prediction, forecast is essential to guarantee not only the sufficient quantity, but also a good water distribution and management system. Beside the fact that several works have been undertaken to address this concern, there is still a considerable disparity between different methods and standard used for water prediction and estimation. Hence this work contrast and compare two well-defined and established methods from two countries (USA and South Africa) to demonstrate the inconsistency when different method and standards are used interchangeably.

Keywords: water scarcity, water estimation, water prediction, water forecast.

Procedia PDF Downloads 167

Authors: Wali Ullah, Muhammad Nishat

Abstract:

The study investigates the extent to which the so called Nelson-Siegel model (DNS) and its extended version that accounts for time varying volatility (DNS-EGARCH) can optimally fit the yield curve and predict its future path in the context of an emerging economy. For the in-sample fit, both models fit the curve remarkably well even in the emerging markets. However, the DNS-EGARCH model fits the curve slightly better than the DNS. Moreover, both specifications of yield curve that are based on the Nelson-Siegel functional form outperform the benchmark VAR forecasts at all forecast horizons. The DNS-EGARCH comes with more precise forecasts than the DNS for the 6- and 12-month ahead forecasts, while the two have almost similar performance in terms of RMSE for the very short forecast horizons.

Keywords: yield curve, forecasting, emerging markets, Kalman filter, EGARCH

Procedia PDF Downloads 512