TAILIEUCHUNG - Water quality classification by artificial neural network - a case study of Dong Nai river, Vietnam
The results of the study showed that GRNN and MLPNN classified excellently water quality. Optimal structure of the MLPNN was H8I4O1 with model error about while the GRNN was error about . Comparing the result of water quality classification between the ANNs and the fuzzy comprehensive evaluation indicated that they were in close agreement with the respective values (the accurate rate of GRNN 100% and 98,5 % of MLPNN). | Vietnam Journal of Science and Technology 55 (4C) (2017) 297-303 WATER QUALITY CLASSIFICATION BY ARTIFICIAL NEURAL NETWORK - A CASE STUDY OF DONG NAI RIVER, VIETNAM Nguyen Hien Than1, *, Che Dinh Ly1, Pham Van Tat2 1 Faculty of Environment and Natural Resources, Thu Dau Mot University, 06 Tran Van On, Phu Hoa, Thu Dau Mot City, Binh Duong 2 Hoa Sen University,08 Nguyen Van Trang, District 1, Ho Chi Minh City * Email: thannh@ Received: 30 June 2017; Accepted for publication: 18 October 2017 ABSTRACT The Dong Nai River is the main source of supplied water for Ho Chi Minh City, Dong Nai, Binh Duong province and other areas. However, the water quality state of the Dong Nai River has been heavily pressured by discharged sources from urban areas, industrial zones, agricultural, domestic activities, etc. In this paper, the authors employed the artificial neural network model (ANNs) to classify water quality of Dong Nai River that apply a new tool to assess water quality in Vietnam. The monitoring data were used for eight years from 2007 to 2014 with 23 monitoring stations. Two neural network models including a multi-layer perceptron (MLPNN) and a generalized regression network (GRNN) were employed to classify water quality of the Dong Nai River. The results of the study showed that GRNN and MLPNN classified excellently water quality. Optimal structure of the MLPNN was H8I4O1 with model error about while the GRNN was error about . Comparing the result of water quality classification between the ANNs and the fuzzy comprehensive evaluation indicated that they were in close agreement with the respective values (the accurate rate of GRNN 100% and 98,5 % of MLPNN). Keywords: artificial neuralnetwork, water quality,classification, Dong Nai River. 1. INTRODUCTION In lately years, water pollution has become the most concerned problem in many countries around the world. The assessment of long–term water quality changes is also a challenged problem .
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