Search results for: erosion prediction
2529 Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network
Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu
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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.Keywords: big data, k-NN, machine learning, traffic speed prediction
Procedia PDF Downloads 3562528 Modeling and Shape Prediction for Elastic Kinematic Chains
Authors: Jiun Jeon, Byung-Ju Yi
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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling
Procedia PDF Downloads 6002527 Prediction on Housing Price Based on Deep Learning
Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang
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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.Keywords: deep learning, convolutional neural network, LSTM, housing prediction
Procedia PDF Downloads 2982526 Evaluation of the Construction of Terraces on a Family Farm in the Municipality of Jaboticabal (SP), Brazil
Authors: Anderson dos Santos Ananias, Matheus Yuji Shigueoka, Roberto Saverio Souza Costa
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Soil and water conservation can be conceptualized as a combination of management and use methods, which have the function of protecting them against deterioration induced by anthropogenic or natural factors. Thus, the objective of this research was to evaluate the rural extension work in soil conservation carried out at Sítio do Alto in Jaboticabal-SP, through the analysis of planimetric data (latitude and longitude coordinates) and altimetric differences of the empirically constructed terraces by the rural producer and with technical guidance from CATI (Coordination of Integral Technical Assistance). A data collection procedure was carried out in the field, with GPS L1/L2, before the construction of five (5) terraces technically level and after their construction. The results showed that the greatest differences were found on terrace one (1), with a maximum latitude difference of 57 meters, the longitude of 23 m, and altitude of 2 m. These results corroborate the observations in the field, in which the presence of a great erosion caused by the incorrect construction of terrace 1 was verified rainwater to the side of the rural property, where the largest erosion furrows with the beginning of gully formation were found.Keywords: GPS, mechanical pratice, surface runoff, erosion
Procedia PDF Downloads 1122525 Sea-Level Rise and Shoreline Retreat in Tainan Coast
Authors: Wen-Juinn Chen, Yi-Phei Chou, Jou-Han Wang
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Tainan coast is suffering from beach erosion, wave overtopping, and lowland flooding; though most of the shoreline has been protected by seawalls, they still threatened by sea level rise. For coastal resources developing, coastal land utilization, and to draft an appropriate mitigate strategy. Firstly; we must assess the impact of beach erosion under a different scenario of climate change. Here, we have used the meteorological data since 1898 to 2012 to prove that the Tainan area did suffer the impact of climate change. The result shows the temperature has been raised to about 1.7 degrees since 1989. Also, we analyzed the tidal data near the Tainan coast (Anpin site and Junjunn site), it shows sea level rising with a rate about 4.1~4.8 mm/year, this phenomenon will have serious impacts on Tainan coastal area, especially it will worsen coastal erosion. So we have used Bruun rule to calculate the shoreline retreated rate at every two decade period since 2012. Wave data and bottom sand diameter D50 were used to calculate the closure depth that will be used in Bruun formula and the active length of the profile is computed by the beach slope and Dean's equilibrium concept. After analysis, we found that in 2020, the shoreline will be retreated about 3.0 to 12 meters. The maximum retreat is happening at Chigu coast. In 2060, average shoreline retreated distance is 22m, but at Chigu and Tsenwen, shoreline may be backward retreat about 70m and will be reached about 130m at 2100, this will cause a lot of coastal land loss to the sea, protect and mitigate project must be quickly performed.Keywords: sea level rise, shoreline, coastal erosion, climate change
Procedia PDF Downloads 4022524 Soil Degradation Processes in Marginal Uplands of Samar Island, Philippines
Authors: Dernie Taganna Olguera
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Marginal uplands are fragile ecosystems in the tropics that need to be evaluated for sustainable utilization and land degradation mitigation. Thus, this study evaluated the dominant soil degradation processes in selected marginal uplands of Samar Island, Philippines; evaluated the important factors influencing soil degradation in the selected sites and identified the indicators of soil degradation in marginal uplands of the tropical landscape of Samar Island, Philippines. Two (2) sites were selected (Sta. Rita, Samar and Salcedo, Eastern, Samar) representing the western and eastern sides of Samar Island respectively. These marginal uplands represent different agro-climatic zones suitable for the study. Soil erosion is the major soil degradation process in the marginal uplands studied. It resulted in not only considerable soil losses but nutrient losses as well. Soil erosion varied with vegetation cover and site. It was much higher in the sweetpotato, cassava, and gabi crops than under natural vegetation. In addition, soil erosion was higher in Salcedo than in Sta. Rita, which is related to climatic and soil characteristics. Bulk density, porosity, aggregate stability, soil pH, organic matter, and carbon dioxide evolution are good indicators of soil degradation. The dominance of Saccharum spontaneum Linn., Imperata cylindrica Linn, Melastoma malabathricum Linn. and Psidium guajava Linn indicated degraded soil condition. Farmer’s practices particularly clean culture and organic fertilizer application influenced the degree of soil degradation in the marginal uplands of Samar Island, Philippines.Keywords: soil degradation, soil erosion, marginal uplands, Samar island, Philippines
Procedia PDF Downloads 4102523 Numerical Prediction of Wall Eroded Area by Cavitation
Authors: Ridha Zgolli, Ahmed Belhaj, Maroua Ennouri
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This study presents a new method to predict cavitation area that may be eroded. It is based on the post-treatment of URANS simulations in cavitant flows. The most RANS calculations with incompressible consideration are based on cavitation model using mixture fluid with density (ρm) calculated as a function of liquid density (ρliq), vapour or gas density (ρvap) and vapour or gas volume fraction α (ρm = αρvap + (1-α) ρliq). The calculations are performed on hydrofoil geometries and compared with experimental works concerning flows characteristics (size of pocket, pressure, velocity). We present here the used cavitation model and the approach followed to evaluate the value of α fixing the shape of pocket around wall before collapsing.Keywords: flows, CFD, cavitation, erosion
Procedia PDF Downloads 3342522 Properties of Compressed Earth Blocks Enhanced with Clay Pozzolana
Authors: Humphrey Danso, Seth Adu
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The high cost of cement and its greenhouse effect on the environment have led to the use of alternative building materials in the production of block and bricks. This study seeks to investigate the properties of compressed earth blocks (CEBs) enhanced with clay pozzolana. CEBs of size 290 × 140 × 100 mm were prepared with 10, 20 and 30 % weight of clay pozzolana. The CEBs were compressed at a constant pressure of 5 MPa and cured for 28 days. The blocks, after 7, 14, 21 and 28 days of curing were tested for density, water absorption, compressive strength and erosion. It was found that amount of pozzolana content did not have much influence on blocks’ density. There was a decline in water absorption of the stabilised blocks ranged between 32.8% and 252.2% over the unstabilised blocks. The highest compressive strength (3.75MPa) of the stabilized blocks was achieved at 28th day of curing with 30% clay pozzolana content, which showed an improvement of 116.8% strength over the unstabilised blocks. Furthermore, there was a statistically significant difference in the erosion resistance between the stabilized blocks and the unstabilised blocks. The study concludes that the inclusion of the clay pozzolana increased the properties of the CEBs, and therefore recommended for use in the building of houses.Keywords: clay pozzolana, compressed earth blocks (CEBs), compressive strength, erosion test
Procedia PDF Downloads 2772521 Urban Growth Prediction Using Artificial Neural Networks in Athens, Greece
Authors: Dimitrios Triantakonstantis, Demetris Stathakis
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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction
Procedia PDF Downloads 5222520 The Effect of the Combination of Mouthwash and Saliva Substitutes on Tooth Erosion: An in Vitro Study
Authors: Young-Eun Jang, Mi-Hye Ma, Yemi Kim
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As the elderly population increases, the number of patients complaining of dry mouth is also increasing. Elderly people often use mouthwash to prevent periodontal disease. Mouthwash and saliva substitutes with low pH were reported to be able to cause enamel erosion. To the best of our knowledge, there have been no studies showing the effect of mouthwash on patients using saliva substitutes. Therefore, the purpose of this study was to evaluate the effect of the use of mouthwash in combination with saliva substitutes on tooth erosion using a quantitative light-induced fluorescence-digital (QLF-D) system. A total of 96 bovine specimens were embedded in putty blocks and randomly allocated to the following groups with n = 12 each: Group 1, application of mouthwash; Group 2, application of saliva substitutes; Group 3, application of saliva substitutes in combination with mouthwash; and control group, application of saline. The bovine samples were eroded using a demineralization solution and then saliva substitutes and mouthwash were applied according to the groups for 2 weeks. For saliva substitutes, three different products were used; Oasis (Oasis Consumer Health, Cleveland, OH, USA), Xeromia solution (Osstem Pharma Co., Seoul, Korea), and Drymund gel (Dong-A Pharma Co., Seoul, Korea). The pH values of the saliva substitutes were determined using a pH meter. Loss of enamel and root dentin was measured using the QLF-D system immediately after demineralization on the 3rd, 7th, and 14th days. The data were analyzed using repeated measures ANOVA followed by Tukey’s post hoc tests (p < 0.05). Mineral loss in enamel and root dentin was detected when mouthwash and saliva substitutes were used alone, respectively (p < 0.05). Also, when mouthwash was used with saliva substitutes, the mineral loss was observed in enamel and root dentin (p < 0.05). The use of Xeromia and Drymund gel increased mineral loss of enamel significantly compared to the use of Oasis (p < 0.05). However, when Drymund gel and Xeromia were used in combination with mouthwash, mineral loss of enamel was significantly reduced compared to when they were used alone (p < 0.05). The pH values of Drymund gel, Xeromia, Oasis, and mouthwash were 5.5, 5.52, 6.2, and 6.37, respectively. Based on these results, it can be concluded that the use of mouthwash with a higher pH value than that of saliva substitutes could help patients suffering from xerostomia avoid the risk of dental erosion.Keywords: saliva substitute, mouthwash, tooth erosion, dry mouth
Procedia PDF Downloads 842519 Virtual Reality Based 3D Video Games and Speech-Lip Synchronization Superseding Algebraic Code Excited Linear Prediction
Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung
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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality
Procedia PDF Downloads 4612518 Cross Project Software Fault Prediction at Design Phase
Authors: Pradeep Singh, Shrish Verma
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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.Keywords: software metrics, fault prediction, cross project, within project.
Procedia PDF Downloads 3342517 Use of Geoinformatics and Mathematical Equations to Assess Erosion and Soil Fertility in Cassava Growing Areas in Maha Sarakham Province, Thailand
Authors: Sasirin Srisomkiew, Sireewan Ratsadornasai, Tanomkwan Tipvong, Isariya Meesing
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Cassava is an important food source in the tropics and has recently gained attention as a potential source of biofuel that can replace limited fossil fuel sources. As a result, the demand for cassava production to support industries both within the country and abroad has increased. In Thailand, most farmers prefer to grow cassava in sandy and sandy loam areas where the soil has low natural fertility. Cassava is a tuber plant that has large roots to store food, resulting in the absorption of large amounts of nutrients from the soil, such as nitrogen, phosphorus, and potassium. Therefore, planting cassava in the same area for a long period causes soil erosion and decreases soil fertility. The loss of soil fertility affects the economy, society, and food and energy security of the country. Therefore, it is necessary to know the level of soil fertility and the amount of nutrients in the soil. To address this problem, this study applies geo-informatics technology and mathematical equations to assess erosion and soil fertility and to analyze factors affecting the amount of cassava production in Maha Sarakham Province. The results show that the area for cassava cultivation has increased in every district of Maha Sarakham Province between 2015-2022, with the total area increasing to 180,922 rai or 5.47% of the province’s total area during this period. Furthermore, it was found that it is possible to assess areas with soil erosion problems that had a moderate level of erosion in areas with high erosion rates ranging from 5-15 T/rai/year. Soil fertility assessment and information obtained from the soil nutrient map for 2015–2023 reveal that farmers in the area have improved the soil by adding chemical fertilizers along with organic fertilizers, such as manure and green manure, to increase the amount of nutrients in the soil. This is because the soil resources of Maha Sarakham Province mostly have relatively low agricultural potential due to the soil texture being sand and sandy loam. In this scenario, the ability to absorb nutrients is low, and the soil holds little water, so it is naturally low in fertility. Moreover, agricultural soil problems were found, including the presence of saline soil, sandy soil, and acidic soil, which is a serious restriction on land use because it affects the release of nutrients into the soil. The results of this study may be used as a guideline for managing soil resources and improving soil quality to prevent soil degradation problems that may occur in the future.Keywords: Cassava, geoinformatics, soil erosion, soil fertility, land use change
Procedia PDF Downloads 382516 A Deep Learning-Based Pedestrian Trajectory Prediction Algorithm
Authors: Haozhe Xiang
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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.Keywords: deep learning, graph convolutional network, attention mechanism, LSTM
Procedia PDF Downloads 612515 Land Degradation Assessment through Spatial Data Integration in Eastern Chotanagpur Plateau, India
Authors: Avijit Mahala
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Present study is primarily concerned with the physical processes and status of land degradation in a tropical plateau fringe. Chotanagpur plateau is one of the most water erosion related degraded areas of India. The granite gneiss geological formation, low to medium developed soil cover, undulating lateritic uplands, high drainage density, low to medium rainfall (100-140cm), dry tropical deciduous forest cover makes the Silabati River basin a truly representative of the tropical environment. The different physical factors have been taken for land degradation study includes- physiographic formations, hydrologic characteristics, and vegetation cover. Water erosion, vegetal degradation, soil quality decline are the major processes of land degradation in study area. Granite-gneiss geological formation is responsible for developing undulating landforms. Less developed soil profile, low organic matter, poor structure of soil causes high soil erosion. High relief and sloppy areas cause unstable environment. The dissected highland causes topographic hindrance in productivity. High drainage density and frequency in rugged upland and intense erosion in sloppy areas causes high soil erosion of the basin. Decreasing rainfall and increasing aridity (low P/PET) threats water stress condition. Green biomass cover area is also continuously declining. Through overlaying the different physical factors (geological formation, soil characteristics, geomorphological characteristics, etc.) of considerable importance in GIS environment the varying intensities of land degradation areas has been identified. Middle reaches of Silabati basin with highly eroded laterite soil cover areas are more prone to land degradation.Keywords: land degradation, tropical environment, lateritic upland, undulating landform, aridity, GIS environment
Procedia PDF Downloads 1322514 Development of Prediction Models of Day-Ahead Hourly Building Electricity Consumption and Peak Power Demand Using the Machine Learning Method
Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas
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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.Keywords: building energy prediction, data mining, demand response, electricity market
Procedia PDF Downloads 3122513 Relationship between Gully Development and Characteristics of Drainage Area in Semi-Arid Region, NW Iran
Authors: Ali Reza Vaezi, Ouldouz Bakhshi Rad
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Gully erosion is a widespread and often dramatic form of soil erosion caused by water during and immediately after heavy rainfall. It occurs when flowing surface water is channelled across unprotected land and washes away the soil along the drainage lines. The formation of gully is influenced by various factors, including climate, drainage surface area, slope gradient, vegetation cover, land use, and soil properties. It is a very important problem in semi-arid regions, where soils have lower organic matter and are weakly aggregated. Intensive agriculture and tillage along the slope can accelerate soil erosion by water in the region. There is little information on the development of gully erosion in agricultural rainfed areas. Therefore, this study was carried out to investigate the relationship between gully erosion and morphometric characteristics of the drainage area and the effects of soil properties and soil management factors (land use and tillage method) on gully development. A field study was done in a 900 km2 agricultural area in Hshtroud township located in the south of East Azarbijan province, NW Iran. Toward this, two hundred twenty-two gullies created in rainfed lands were found in the area. Some properties of gullies, consisting of length, width, depth, height difference, cross section area, and volume, were determined. Drainage areas for each or some gullies were determined, and their boundaries were drawn. Additionally, the surface area of each drainage, land use, tillage direction, and soil properties that may affect gully formation were determined. The soil erodibility factor (K) defined in the Universal Soil Loss Equation (USLE) was estimated based on five soil properties (silt and very fine sand, coarse sand, organic matter, soil structure code, and soil permeability). Gully development in each drainage area was quantified using its volume and soil loss. The dependency of gully development on drainage area characteristics (surface area, land use, tillage direction, and soil properties) was determined using correlation matrix analysis. Based on the results, gully length was the most important morphometric characteristic indicating the development of gully erosion in the lands. Gully development in the area was related to slope gradient (r= -0.26), surface area (r= 0.71), the area of rainfed lands (r= 0.23), and the area of rainfed tilled along the slope (r= 0.24). Nevertheless, its correlation with the area of pasture and soil erodibility factor (K) was not significant. Among the characteristics of drainage area, surface area is the major factor controlling gully volume in the agricultural land. No significant correlation was found between gully erosion and soil erodibility factor (K) estimated by the Universal Soil Loss Equation (USLE). It seems the estimated soil erodibility can’t describe the susceptibility of the study soils to the gully erosion process. In these soils, aggregate stability and soil permeability are the two soil physical properties that affect the actual soil erodibility and in consequence, these soil properties can control gully erosion in the rainfed lands.Keywords: agricultural area, gully properties, soil structure, USLE
Procedia PDF Downloads 712512 Prediction of CO2 Concentration in the Korea Train Express (KTX) Cabins
Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park
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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering
Procedia PDF Downloads 5372511 Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors
Authors: Katawut Kaewbanjong
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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.Keywords: prediction model, statistical analysis, software project, user satisfaction factor
Procedia PDF Downloads 1172510 Calibration Model of %Titratable Acidity (Citric Acid) for Intact Tomato by Transmittance SW-NIR Spectroscopy
Authors: K. Petcharaporn, S. Kumchoo
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The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid
Procedia PDF Downloads 2662509 Ground Surface Temperature History Prediction Using Long-Short Term Memory Neural Network Architecture
Authors: Venkat S. Somayajula
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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical featuresKeywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle
Procedia PDF Downloads 1242508 Modeling Stream Flow with Prediction Uncertainty by Using SWAT Hydrologic and RBNN Neural Network Models for Agricultural Watershed in India
Authors: Ajai Singh
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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation
Procedia PDF Downloads 3572507 Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients
Authors: Soha A. Bahanshal, Byung G. Kim
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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission
Procedia PDF Downloads 1792506 Using High Performance Computing for Online Flood Monitoring and Prediction
Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic
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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization
Procedia PDF Downloads 4872505 Prediction of PM₂.₅ Concentration in Ulaanbaatar with Deep Learning Models
Authors: Suriya
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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar
Procedia PDF Downloads 412504 Life Prediction of Condenser Tubes Applying Fuzzy Logic and Neural Network Algorithms
Authors: A. Majidian
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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.Keywords: life prediction, condenser tube, neural network, fuzzy logic
Procedia PDF Downloads 3452503 Wind Speed Prediction Using Passive Aggregation Artificial Intelligence Model
Authors: Tarek Aboueldahab, Amin Mohamed Nassar
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Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction
Procedia PDF Downloads 4422502 Erosion Wear of Cast Al-Si Alloys
Authors: Pooja Verma, Rajnesh Tyagi, Sunil Mohan
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Al-Si alloys are widely used in various components such as liner-less engine blocks, piston, compressor bodies and pumps for automobile sector and aerospace industries due to their excellent combination of properties like low thermal expansion coefficient, low density, excellent wear resistance, high corrosion resistance, excellent cast ability, and high hardness. The low density and high hardness of primary Si phase results in significant reduction in density and improvement in wear resistance of hypereutectic Al-Si alloys. Keeping in view of the industrial importance of the alloys, hypereutectic Al-Si alloys containing 14, 16, 18 and 20 wt. % of Si were prepared in a resistance furnace using adequate amount of deoxidizer and degasser and their erosion behavior was evaluated by conducting tests at impingement angles of 30°, 60°, and 90° with an erodent discharge rate of 7.5 Hz, pressure 1 bar using erosion test rig. Microstructures of the cast alloys were examined using Optical microscopy (OM) and scanning electron microscopy (SEM) and the presence of Si particles was confirmed by x-ray diffractometer (XRD). The mechanical properties and hardness were measured using uniaxial tension tests at a strain rate of 10-3/s and Vickers hardness tester. Microstructures of the alloys and X-ray examination revealed the presence of primary and eutectic Si particles in the shape of cuboids or polyhedral and finer needles. Yield strength (YS), ultimate tensile strength (UTS), and uniform elongation of the hypereutectic Al-Si alloys were observed to increase with increasing content of Si. The optimal strength and ductility was observed for Al-20 wt. % Si alloy which is significantly higher than the Al-14 wt. % Si alloy. The increased hardness and the strength of the alloys with increasing amount of Si has been attributed presence of Si in the solid solution which creates strain, and this strain interacts with dislocations resulting in solid-solution strengthening. The interactions between distributed primary Si particles and dislocations also provide Orowan strengthening leading to increased strength. The steady state erosion rate was found to decrease with increasing angle of impact as well as Si content for all the alloys except at 900 where it was observed to increase with the increase in the Si content. The minimum erosion rate is observed in Al-20 wt. % Si alloy at 300 and 600 impingement angles because of its higher hardness in comparison to other alloys. However, at 90° impingement angle the wear rate for Al-20 wt. % Si alloy is found to be the minimum due to deformation, subsequent cracking and chipping off material.Keywords: Al-Si alloy, erosion wear, cast alloys, dislocation, strengthening
Procedia PDF Downloads 612501 SNR Classification Using Multiple CNNs
Authors: Thinh Ngo, Paul Rad, Brian Kelley
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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.Keywords: classification, CNN, deep learning, prediction, SNR
Procedia PDF Downloads 1292500 Evaluation of Spatial Distribution Prediction for Site-Scale Soil Contaminants Based on Partition Interpolation
Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei
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Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty
Procedia PDF Downloads 139