Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 6

Search results for: S. Areerachakul

6 Classifying Students for E-Learning in Information Technology Course Using ANN

Authors: S. Areerachakul, N. Ployong, S. Na Songkla

Abstract:

This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by Electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: Artificial neural network, classification, students.

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5 The Using Artificial Neural Network to Estimate of Chemical Oxygen Demand

Authors: S. Areerachakul

Abstract:

Nowadays, the increase of human population every year results in increasing of water usage and demand. Saen Saep canal is important canal in Bangkok. The main objective of this study is using Artificial Neural Network (ANN) model to estimate the Chemical Oxygen Demand (COD) on data from 11 sampling sites. The data is obtained from the Department of Drainage and Sewerage, Bangkok Metropolitan Administration, during 2007-2011. The twelve parameters of water quality are used as the input of the models. These water quality indices affect the COD. The experimental results indicate that the ANN model provides a high correlation coefficient (R=0.89).

Keywords: Artificial neural network, chemical oxygen demand, estimate, surface water.

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4 The Coupling of Photocatalytic Oxidation Processes with Activated Carbon Technologies and the Comparison of the Treatment Methods for Organic Removal from Surface Water

Authors: N. Areerachakul

Abstract:

The surface water used in this study was collected from the Chao Praya River at the lower part at the Nonthaburi bridge. It was collected and used throughout the experiment. TOC (also known as DOC) in the range between 2.5 to 5.6 mg/l were investigated in this experiment. The use of conventional treatment methods such as FeCl3 and PAC showed that TOC removal was 65% using FeCl3 and 78% using PAC (powder activated carbon). The advanced oxidation process alone showed only 35% removal of TOC. Coupling advanced oxidation with a small amount of PAC (0.05g/L) increased efficiency by upto 55%. The combined BAC with advanced oxidation process and small amount of PAC demonstrated the highest efficiency of up to 95% of TOC removal and lower sludge production compared with other methods.

Keywords: Advanced oxidation process, TOC, PAC

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3 Overviews of Rainwater Harvesting and Utilization in Thailand: Bangsaiy Municipality

Authors: N. Areerachakul

Abstract:

In developing countries located in monsoon areas like Thailand where rainwater is currently of no value for urban dwellers due to easily access to piped water supply at each household, studies in rainwater harvesting for domestic use are of low interest. However it is needed to undertake research to find out appropriate rainwater harvesting systems particularly for small urban communities that are recently developed from a full rural structure to urban context. As a matter of fact, in such transitional period, relying on only common water resources is risky. With some specific economic settings, land use patterns, and historical and cultural context that dominate perceptions of water users in the study area, the level of service in this study may certainly be different from megacities or cities located in industrial zone. The overviews of some available technologies and background of rainwater harvesting including alternate resource are included in this paper. Among other sources of water supply, ground water use as the water resource of Thailand and also in the study area.

Keywords: Developing country, water supply, rainwater, ground water.

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2 Application of Artificial Neural Network to Classification Surface Water Quality

Authors: S. Wechmongkhonkon, N.Poomtong, S. Areerachakul

Abstract:

Water quality is a subject of ongoing concern. Deterioration of water quality has initiated serious management efforts in many countries. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of canals in Dusit district in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 96.52% in classifying the water quality of Dusit district canal in Bangkok Subsequently, this encouraging result could be applied with plan and management source of water quality.

Keywords: artificial neural network, classification, surface water quality

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1 Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water

Authors: S. Areerachakul

Abstract:

Nowadays, several techniques such as; Fuzzy Inference System (FIS) and Neural Network (NN) are employed for developing of the predictive models to estimate parameters of water quality. The main objective of this study is to compare between the predictive ability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model and Artificial Neural Network (ANN) model to estimate the Biochemical Oxygen Demand (BOD) on data from 11 sampling sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage, Bangkok Metropolitan Administration, during 2004-2011. The five parameters of water quality namely Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen (NO3N), and Total Coliform bacteria (T-coliform) are used as the input of the models. These water quality indices affect the biochemical oxygen demand. The experimental results indicate that the ANN model provides a higher correlation coefficient (R=0.73) and a lower root mean square error (RMSE=4.53) than the corresponding ANFIS model.

Keywords: adaptive neuro-fuzzy inference system, artificial neural network, biochemical oxygen demand, surface water.

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