Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area -- Journal of Oceanology and Limnology,2020,38(6):1835-1845

	Cite this paper:
	LU Fang, ZHANG Haoqing, LIU Wenquan. Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area[J]. Journal of Oceanology and Limnology, 2020, 38(6): 1835-1845

Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area

LU Fang^1,2,3, ZHANG Haoqing^1,3, LIU Wenquan^2,4

1 Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering, Ocean University of China, Qingdao 266100, China;
2 Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266000, China;
3 Key Laboratory of Marine Environment and Ecology, Ministry of Education, Qingdao 266100, China;
4 Key Laboratory of Marine Sedimentology and Environmental Geology, First Institute of Oceanography, Ministry of Natural Resources(MNR), Qingdao 266061, China

Abstract:

Artificial Neural Network (ANN) models have been extensively applied in the prediction of water resource variables, and Geographical Information System (GIS) includes powerful functions to visualize spatial data. In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules, a GIS-based ANN water quality prediction system was developed in the present study. The ANN module and ArcGIS Engine module, along with a dynamic database, were imbedded in the system, which integrates water quality prediction via the ANN model and spatial presentation of the model results. The structure of the ANN model could be modified through the graphical user interface to optimize the model performance. The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area. The prediction results were verified with the monitoring data, and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods. Overall, the developed system provided satisfactory prediction results, and spatial distribution maps of the predicted results were obtained, which coincided with the monitored values. The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.

Key words: water quality prediction|Geographical Information System (GIS)|artificial neural network|integration|system development

Received: 2019-07-10 Revised: 2019-09-16

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