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

作     者：LU Fang ZHANG Haoqing LIU Wenquan LU Fang;ZHANG Haoqing;LIU Wenquan

作者机构：Shandong Provincial Key Laboratory of Marine Environment and Geological EngineeringOcean University of ChinaQingdao 266100China Laboratory for Marine GeologyQingdao National Laboratory for Marine Science and TechnologyQingdao 266000China Key Laboratory of Marine Environment and EcologyMinistry of EducationQingdao 266100China Key Laboratory of Marine Sedimentology and Environmental GeologyFirst Institute of OceanographyMinistry of Natural Resources(MNR)Qingdao 266061China 

出 版 物：《Journal of Oceanology and Limnology》 (海洋湖沼学报（英文）)

年 卷 期：2020年第38卷第6期

页      面：1835-1845页

核心收录：

学科分类：0710[理学-生物学] 0908[农学-水产] 08[工学] 081104[工学-模式识别与智能系统] 0707[理学-海洋科学] 0815[工学-水利工程] 0811[工学-控制科学与工程] 

基　　金：Supported by the National Natural Science Foundation of China(Nos.41807247,41807229) the Special Fund for Shandong Post-doctoral Innovation Project。 

主　　题：water quality prediction Geographical Information System(GIS) artificial neural network integration system development 

摘      要：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.