Search results for: meteorological prediction data

Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Nokwethaba Makhanya, Babatunde J. Abiodun, Piotr Wolski

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Climate change possibly intensifies hydrological droughts and reduces water availability in river basins. Despite this, most research on climate change effects in southern Africa has focused exclusively on meteorological droughts. This thesis projects the potential impact of climate change on the future characteristics of hydrological droughts in the Limpopo River Basin (LRB). The study uses regional climate model (RCM) measurements (from the Coordinated Regional Climate Downscaling Experiment, CORDEX) and a combination of hydrological simulations (using the Soil and Water Assessment Tool Plus model, SWAT+) to predict the impacts at four global warming levels (GWLs: 1.5℃, 2.0℃, 2.5℃, and 3.0℃) under the RCP8.5 future climate scenario. The SWAT+ model was calibrated and validated with a streamflow dataset observed over the basin, and the sensitivity of model parameters was investigated. The performance of the SWAT+LRB model was verified using the Nash-Sutcliffe efficiency (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) have been used to detect meteorological droughts. The Soil Water Index (SSI) has been used to define agricultural drought, while the Water Yield Drought Index (WYLDI), the Surface Run-off Index (SRI), and the Streamflow Index (SFI) have been used to characterise hydrological drought. The performance of the SWAT+ model simulations over LRB is sensitive to the parameters CN2 (initial SCS runoff curve number for moisture condition II) and ESCO (soil evaporation compensation factor). The best simulation generally performed better during the calibration period than the validation period. In calibration and validation periods, NSE is ≤ 0.8, while PBIAS is ≥ ﹣80.3%, RMSE ≥ 11.2 m³/s, and R² ≤ 0.9. The simulations project a future increase in temperature and potential evapotranspiration over the basin, but they do not project a significant future trend in precipitation and hydrological variables. However, the spatial distribution of precipitation reveals a projected increase in precipitation in the southern part of the basin and a decline in the northern part of the basin, with the region of reduced precipitation projected to increase with GWLs. A decrease in all hydrological variables is projected over most parts of the basin, especially over the eastern part of the basin. The simulations predict meteorological droughts (i.e., SPEI and SPI), agricultural droughts (i.e., SSI), and hydrological droughts (i.e., WYLDI, SRI) would become more intense and severe across the basin. SPEI-drought has a greater magnitude of increase than SPI-drought, and agricultural and hydrological droughts have a magnitude of increase between the two. As a result, this research suggests that future hydrological droughts over the LRB could be more severe than the SPI-drought projection predicts but less severe than the SPEI-drought projection. This research can be used to mitigate the effects of potential climate change on basin hydrological drought.

Keywords: climate change, CORDEX, drought, hydrological modelling, Limpopo River Basin

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Authors: Tang Wei, Yang Xiaofeng, Gui Yewei, Du Yanxia

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Complex lifting entry was selected for precise landing performance during the Mars Science Laboratory entry. This study aims to develop the three-dimensional numerical method for precise computation and the surface panel method for rapid engineering prediction. Detailed flow field analysis for Mars exploration mission was performed by carrying on a series of fully three-dimensional Navier-Stokes computations. The static aerodynamic performance was then discussed, including the surface pressure, lift and drag coefficient, lift-to-drag ratio with the numerical and engineering method. Computation results shown that the shock layer is thin because of lower effective specific heat ratio, and that calculated results from both methods agree well with each other, and is consistent with the reference data. Aerodynamic performance analysis shows that CG location determines trim characteristics and pitch stability, and certain radially and axially shift of the CG location can alter the capsule lifting entry performance, which is of vital significance for the aerodynamic configuration des0ign and inner instrument layout of the Mars entry capsule.

Keywords: Mars entry capsule, static aerodynamics, computational fluid dynamics, hypersonic

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Authors: Dawei Xia, Benyan Jiang, Yong Li

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Split-type air conditioners is widely used for indoor temperature regulation in urban residential buildings in summer in China. The energy-related occupant behavior has a great impact on building energy consumption. Obtaining the energy-related occupant behavior data of air conditioners is the research basis for the energy consumption prediction and simulation. Relying on the development of sensing and control technology, this paper selects Zigbee intelligent electronic equipment to monitor the energy-related occupant behavior of 20 households for 3 months in summer. Through analysis of data, it is found that people of different ages in the region have significant difference in the time, duration, frequency, and energy consumption of air conditioners, and form a data model of three basic energy-related occupant behavior patterns to provide an accurate simulation of energy.

Keywords: occupant behavior, Zigbee, split air conditioner, energy simulation

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Authors: Arragaw Alemayehu, Woldeamlak Bewket

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The aim of this study was to understand the influence of current climate variability on crop production in the central highlands of Ethiopia. We used monthly rainfall and temperature data from 132 points each representing a pixel of 10×10 km. The data are reconstructions based on station records and meteorological satellite observations. Production data of the five major crops in the area were collected from the Central Statistical Agency for the period 2004-2013 and for the main cropping season, locally known as Meher. The production data are at the Enumeration Area (EA ) level and hence the best available dataset on crop production. The results show statistically significant decreasing trends in March–May (Belg) rainfall in the area. However, June – September (Kiremt) rainfall showed increasing trends in Efratana Gidim and Menz Gera Meder which the latter is statistically significant. Annual rainfall also showed positive trends in the area except Basona Werana where significant negative trends were observed. On the other hand, maximum and minimum temperatures showed warming trends in the study area. Correlation results have shown that crop production and area of cultivation have positive correlation with rainfall, and negative with temperature. When the trends in crop production are investigated, most crops showed negative trends and below average production was observed. Regression results have shown that rainfall was the most important determinant of crop production in the area. It is concluded that current climate variability has a significant influence on crop production in the area and any unfavorable change in the local climate in the future will have serious implications for household level food security. Efforts to adapt to the ongoing climate change should begin from tackling the current climate variability and take a climate risk management approach.

Keywords: central highlands, climate variability, crop production, Ethiopia, regression, trend

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Authors: Pedro B. Antunes, Paulo J. Ramísio

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Road runoff is a linear source of diffuse pollution that can cause significant environmental impacts. During rainfall events, pollutants from both stationary and mobile sources, which have accumulated on the road surface, are dragged through the superficial runoff. Road runoff in coastal zones may present high levels of salinity and chlorides due to the proximity of the sea and transported marine aerosols. Appearing to be correlated to this process, organic matter concentration may also be significant. This study assesses this phenomenon with the purpose of identifying the relationships between monitored water quality parameters and intrinsic site variables. To achieve this objective, an extensive monitoring program was conducted on a Portuguese coastal highway. The study included thirty rainfall events, in different weather, traffic and salt deposition conditions in a three years period. The evaluations of various water quality parameters were carried out in over 200 samples. In addition, the meteorological, hydrological and traffic parameters were continuously measured. The salt deposition rates (SDR) were determined by means of a wet candle device, which is an innovative feature of the monitoring program. The SDR, variable throughout the year, appears to show a high correlation with wind speed and direction, but mostly with wave propagation, so that it is lower in the summer, in spite of the favorable wind direction in the case study. The distance to the sea, topography, ground obstacles and the platform altitude seems to be also relevant. It was confirmed the high salinity in the runoff, increasing the concentration of the water quality parameters analyzed, with significant amounts of seawater features. In order to estimate the correlations and patterns of different water quality parameters and variables related to weather, road section and salt deposition, the study included exploratory data analysis using different techniques (e.g. Pearson correlation coefficients, Cluster Analysis and Principal Component Analysis), confirming some specific features of the investigated road runoff. Significant correlations among pollutants were observed. Organic matter was highlighted as very dependent of salinity. Indeed, data analysis showed that some important water quality parameters could be divided into two major clusters based on their correlations to salinity (including organic matter associated parameters) and total suspended solids (including some heavy metals). Furthermore, the concentrations of the most relevant pollutants seemed to be very dependent on some meteorological variables, particularly the duration of the antecedent dry period prior to each rainfall event and the average wind speed. Based on the results of a monitoring case study, in a coastal zone, it was proven that SDR, associated with the hydrological characteristics of road runoff, can contribute for a better knowledge of the runoff characteristics, and help to estimate the specific nature of the runoff and related water quality parameters.

Keywords: coastal zones, monitoring, road runoff pollution, salt deposition

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Authors: Brahim Fares Zaidi

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Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: ARSDS, HTK, HMM, MFCC, PLP

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Authors: Machimontorn Promtong, Sherman C. P. Cheung, Guan H. Yeoh, Sara Vahaji, Jiyuan Tu

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The wide range of industrial applications involved with boiling flows promotes the necessity of establishing fundamental knowledge in boiling flow phenomena. For this purpose, a number of experimental and numerical researches have been performed to elucidate the underlying physics of this flow. In this paper, the improved wall boiling models, implemented on ANSYS CFX 14.5, were introduced to study subcooled boiling flow at elevated pressure. At the heated wall boundary, the Fractal model, Force balance approach and Mechanistic frequency model are given for predicting the nucleation site density, bubble departure diameter, and bubble departure frequency. The presented wall heat flux partitioning closures were modified to consider the influence of bubble sliding along the wall before the lift-off, which usually happens in the flow boiling. The simulation was performed based on the Two-fluid model, where the standard k-ω SST model was selected for turbulence modelling. Existing experimental data at around 5 bars were chosen to evaluate the accuracy of the presented mechanistic approach. The void fraction and Interfacial Area Concentration (IAC) are in good agreement with the experimental data. However, the predicted bubble velocity and Sauter Mean Diameter (SMD) are over-predicted. This over-prediction may be caused by consideration of only dispersed and spherical bubbles in the simulations. In the future work, the important physical mechanisms of bubbles, such as merging and shrinking during sliding on the heated wall will be incorporated into this mechanistic model to enhance its capability for a wider range of flow prediction.

Keywords: subcooled boiling flow, computational fluid dynamics (CFD), mechanistic approach, two-fluid model

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Acumsiri Tantikarnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020. Using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for nineteen selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (Labor Force Survey: LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: income inequality, nighttime light, population density, Thailand, urbanization

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Authors: Elda Maraj, Shkelqim Kuka

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Coronary heart disease causes many deaths in the world. Unfortunately, this problem will continue to increase in the future. In this paper, a fuzzy logic model to predict coronary heart disease is presented. This model has been developed with seven input variables and one output variable that was implemented for 30 patients in Albania. Here fuzzy logic toolbox of MATLAB is used. Fuzzy model inputs are considered as cholesterol, blood pressure, physical activity, age, BMI, smoking, and diabetes, whereas the output is the disease classification. The fuzzy sets and membership functions are chosen in an appropriate manner. Centroid method is used for defuzzification. The database is taken from University Hospital Center "Mother Teresa" in Tirana, Albania.

Keywords: coronary heart disease, fuzzy logic toolbox, membership function, prediction model

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Abdullah Al Faruque, Ram Balachandar

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Series of laboratory tests were carried out to study the extent of scour caused by a three-dimensional wall jets exiting from a square cross-section nozzle and into a non-cohesive sand beds. Previous observations have indicated that the effect of the tailwater depth was significant for densimetric Froude number greater than ten. However, the present results indicate that the cut off value could be lower depending on the value of grain size-to-nozzle width ratio. Numbers of equations are drawn out for a better scaling of numerous scour parameters. Also suggested the empirical prediction of scour to predict the scour centre line profile and plan view of scour profile at any particular time.

Keywords: densimetric froude number, jets, nozzle, sand, scour, tailwater, time

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Elison Eduardo Jardim Bierhals, Claudineia Brazil, Rafael Haag, Elton Rossini

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This work aims to validate and make the projections of solar energy for the Brazilian period from 2025 to 2100. As the plants designed by the HadGEM2-AO (Global Hadley Model 2 - Atmosphere) General Circulation Model UK Met Office Hadley Center, belonging to Phase 5 of the Intercomparison of Coupled Models (CMIP5). The simulation results of the model are compared with monthly data from 2006 to 2013, measured by a network of meteorological sections of the National Institute of Meteorology (INMET). The performance of HadGEM2-AO is evaluated by the efficiency coefficient (CEF) and bias. The results are shown in the table of maps and maps. HadGEM2-AO, in the most pessimistic scenario, RCP 8.5 had a very good accuracy, presenting efficiency coefficients between 0.94 and 0.98, the perfect setting being Solar radiation, which indicates a horizontal trend, is a climatic alternative for some regions of the Brazilian scenario, especially in spring.

Keywords: climate change, projections, solar radiation, scenarios climate change

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Christopher Deekia Nwimae, Nigel Simms, Liyun Lao

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Erosion in pipe bends caused by particles is a major obstacle in the oil and gas fields and might cause the breakdown of production equipment. This work studied the effects imposed by flow velocity and impact of solid particles diameter in an elbow; erosion rate was verified with experimental data using the computational fluid dynamics (CFD) approach. Two-way coupled Euler-Lagrange and discrete phase model was employed to calculate the air/solid particle flow in an elbow. One erosion model and three-particle rebound models were used to predict the erosion rate on the 90° elbows. The generic erosion model was used in the CFD-based erosion model, and after comparing it with experimental data, results showed agreement with the CFD-based predictions as observed.

Keywords: erosion, prediction, elbow, computational fluid dynamics

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Authors: Mekonen G., Sileshi M., Melkamu M.

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Recharge estimation is important for the assessment and management of groundwater resources effectively. This study applied the soil moisture balance and Baseflow separation methods to estimate groundwater recharge in the Fetam Catchment. It is one of the major catchments understudied from the different catchments in the upper Blue Nile River basin. Surface water has been subjected to high seasonal variation; due to this, groundwater is a primary option for drinking water supply to the community. This research has been conducted to estimate groundwater recharge by using fifteen years of River flow data for the Baseflow separation and ten years of daily meteorological data for the daily soil moisture balance recharge estimating method. The recharge rate by the two methods is 170.5 and 244.9mm/year daily soil moisture and baseflow separation method, respectively, and the average recharge is 207.7mm/year. The average value of annual recharge in the catchment is almost equal to the average recharge in the country, which is 200mm/year. So, each method has its own limitations, and taking the average value is preferable rather than taking a single value. Baseflow provides overestimated result compared to the average of the two, and soil moisture balance is the list estimator. The recharge estimation in the area also should be done by other recharge estimation methods.

Keywords: groundwater, recharge, baseflow separation, soil moisture balance, Fetam catchment

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Authors: Shyamli Singh, Ishupinder Kaur, Vinod K. Sharma

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Climate Change refers to the change in climate for extended period of time. Climate is changing from the past history of earth but anthropogenic activities accelerate this rate of change and which is now being a global issue. Increase in greenhouse gas emissions is causing global warming and climate change related issues at an alarming rate. Increasing temperature results in climate variability across the globe. Changes in rainfall patterns, intensity and extreme events are some of the impacts of climate change. Rainfall variability refers to the degree to which rainfall patterns varies over a region (spatial) or through time period (temporal). Temporal rainfall variability can be directly or indirectly linked to climate change. Such variability in rainfall increases the vulnerability of communities towards climate change. Increasing urbanization and unplanned developmental activities, the air quality is deteriorating. This paper mainly focuses on the rainfall variability due to increasing level of greenhouse gases. Rainfall data of 65 years (1951-2015) of Safdarjung station of Delhi was collected from Indian Meteorological Department and analyzed using Mann-Kendall test for time-series data analysis. Mann-Kendall test is a statistical tool helps in analysis of trend in the given data sets. The slope of the trend can be measured through Sen’s slope estimator. Data was analyzed monthly, seasonally and yearly across the period of 65 years. The monthly rainfall data for the said period do not follow any increasing or decreasing trend. Monsoon season shows no increasing trend but here was an increasing trend in the pre-monsoon season. Hence, the actual rainfall differs from the normal trend of the rainfall. Through this analysis, it can be projected that there will be an increase in pre-monsoon rainfall than the actual monsoon season. Pre-monsoon rainfall causes cooling effect and results in drier monsoon season. This will increase the vulnerability of communities towards climate change and also effect related developmental activities.

Keywords: greenhouse gases, Mann-Kendall test, rainfall variability, Sen's slope

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Authors: Nato Kutaladze, George Mikuchadze, Giorgi Sokhadze

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Precipitation forecasts are one of the most demanding applications in numerical weather prediction (NWP). Georgia, as the whole Caucasian region, is characterized by very complex topography. The country territory is prone to flash floods and mudflows, quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) at any leading time are very important for Georgia. In this study, advanced research weather forecasting model’s skill in QPF is investigated over Georgia’s territory. We have analyzed several convection parameterization and microphysical scheme combinations for different rainy episodes and heavy rainy phenomena. We estimate errors and biases in accumulated 6 h precipitation using different spatial resolution during model performance verification for 12-hour and 24-hour lead time against corresponding rain gouge observations and satellite data. Various statistical parameters have been calculated for the 8-month comparison period, and some skills of model simulation have been evaluated. Our focus is on the formation and organization of convective precipitation systems in a low-mountain region. Several problems in connection with QPF have been identified for mountain regions, which include the overestimation and underestimation of precipitation on the windward and lee side of the mountains, respectively, and a phase error in the diurnal cycle of precipitation leading to the onset of convective precipitation in model forecasts several hours too early.

Keywords: extremal dependence index, false alarm, numerical weather prediction, quantitative precipitation forecasting

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Authors: Dariush Jafari, S. Mostafa Nowee

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In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Omolara Lade, David Oloke

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Ibadan City suffers serious water supply problems; cases of dry taps are common in virtually every part of the City. The scarcity of piped water has made communities find alternative water sources; groundwater sources being a ready source. These wells are prone to pollution due to the close proximity of septic tanks to wells, disposal of solid or liquid wastes in pits, abandoned boreholes or even stream channels and landfills. Storms and floods in Ibadan have increased with consequent devastating effects claiming over 120 lives and displacing 600 people on August 2011 alone. In this study, an analysis of the water demand and sources of supply for the city was carried out through questionnaire survey and collection of data from City’s main water supply - Water Corporation of Oyo State (WCOS), groundwater sources were explored and 30 years rainfall data were collected from Meteorological station in Ibadan. 1067 questionnaire were administered at household level with a response rate of 86.7 %. A descriptive analysis of the survey revealed that 77.1 % of the respondents did not receive water at all from WCOS while 83.8 % depend on groundwater sources. Analysis of data from WCOS revealed that main water supply is inadequate as < 10 % of the population water demand was met. Rainfall intensity is highest in June with a mean value of 188 mm, which can be harvested at community—based level and used to complement the population water demand. Rainwater harvesting if planned, and managed properly will become a valuable alternative source of managing urban flood and alleviating water scarcity in the city.

Keywords: Ibadan, rainwater harvesting, sustainable water, urban flooding

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Esra Polat

Abstract:

Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, demographic indicators, economic indicators, European Union, partial least squares discriminant analysis

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Authors: Jong Won, Park, Sung Hyun, Kim

Abstract:

The kinetic energy to pass subjected to shock and chisel reciprocating piston hydraulic power supplied by the excavator using for the purpose of crushing the rock, and roads, buildings, etc., hydraulic breakers blow. Impact frequency, efficiency measurement of the impact energy, hydraulic breakers, to demonstrate the ability of hydraulic breaker manufacturers and users to a very important item. And difficult in order to confirm the initial performance degradation in the life of the hydraulic breaker has been thought to be a problem.In this study, we measure the efficiency of hydraulic breaker, Impact energy and Impact frequency, the degradation analysis of research to predict the life.

Keywords: impact energy, impact frequency, hydraulic breaker, life prediction

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Authors: Deepak Raj Bhat, Ryuichi Yatabe

Abstract:

In this study, a residual-state creep failure model was developed based on the residual-state creep test results of clayey soils. To develop the proposed model, the regression analyses were done by using the R. The model results of the failure time (tf) and critical displacement (δc) were compared with experimental results and found in close agreements to each others. It is expected that the proposed regression model for residual-state creep failure will be more useful for the prediction of displacement of different clayey soils in the future.

Keywords: regression model, residual-state creep failure, displacement prediction, clayey soils

Procedia PDF Downloads 381

Authors: Hassan Baji, Chun-Qing Li, Wei Yang

Abstract:

Water seepage is one of the most common causes of damage in tunnel structures, which can cause direct and indirect e.g. reinforcement corrosion and calcium leaching damages. Estimation of water seepage or inflow is one of the main challenges in probabilistic assessment of tunnels. The methodology proposed in this study is an attempt for mathematically modeling the water seepage in tunnel structures and further predicting its service life. Using the time-dependent reliability, water seepage is formulated as a failure mode, which can be used for prediction of service life. Application of the formulated seepage failure mode to a case study tunnel is presented.

Keywords: water seepage, tunnels, time-dependent reliability, service life

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Authors: Diana Katherine Gonzalez Galindo, David Rolando Suarez Mora

Abstract:

With this study, we used data mining as a new alternative of the solution to evaluate the comments of the customers in order to find a pattern that helps us to determine some behaviors to reduce the deactivation of the partners of the LEVEL app. In one of the greatest business created in the last times, the partners are being affected due to an internal process that compensates the customer for a bad experience, but these comments could be false towards the driver, that’s why we made an investigation to collect information to restructure this process, many partners have been disassociated due to this internal process and many of them refuse the comments given by the customer. The main methodology used in this case study is the observation, we recollect information in real time what gave us the opportunity to see the most common issues to get the most accurate solution. With this new process helped by data mining, we could get a prediction based on the behaviors of the customer and some basic data recollected such as the age, the gender, and others; this could help us in future to improve another process. This investigation gives more opportunities to the partner to keep his account active even if the customer writes a message through the app. The term is trying to avoid a recession of drivers in the future offering improving in the processes, at the same time we are in search of stablishing a strategy which benefits both the app’s managers and the associated driver.

Keywords: agent, driver, deactivation, rider

Procedia PDF Downloads 263