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 Fang1,2,3, ZHANG Haoqing1,3, LIU Wenquan2,4
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
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
PDF (2979 KB) Free
Print this page
Add to favorites
Email this article to others
Articles by LU Fang
Articles by ZHANG Haoqing
Articles by LIU Wenquan
Al-Sabhan W, Mulligan M, Blackburn G A. 2003. A realtime hydrological model for flood prediction using GIS and the WWW. Computers, Environment and Urban Systems, 27(1):9-32,
Azimi S, Azhdary Moghaddam M, Hashemi Monfared S A. 2019. Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering. Journal of Contaminant Hydrology, 220:6-17,
Brandmeyer J E, Karimi H A. 2000. Coupling methodologies for environmental models. Environmental Modelling & Software, 15(5):479-488,
Cho S, Lim B, Jung J, Kim S, Chae H, Park J, Park S, Park J K. 2014. Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network. Measurement, 53:224-233,
Debaine F, Robin M. 2012. A new GIS modelling of coastal dune protection services against physical coastal hazards. Ocean & Coastal Management, 63:43-54,
Deperlioglu O, Kose U. 2011. An educational tool for artificial neural networks. Computers & Electrical Engineering, 37(3):392-402,
Emerson D G, Vecchia A V, Dahl A L. 2005. Evaluation of Drainage-Area Ratio Method Used to Estimate Streamflow for the Red River of the North Basin, North Dakota and Minnesota. U.S. Department of the Interior, U.S. Geological Survey, Reston, VA.
García-Alba J, Bárcena J F, Ugarteburu C, García A. 2019. Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries. Water Research, 150:283-295,
Ghebremichael L T, Veith T L, Watzin M C. 2010. Determination of critical source areas for phosphorus loss:Lake Champlain basin, Vermont. Transactions of the ASABE, 53(5):1 595-1 604,
Ho C I, Lin M D, Lo S L. 2010. Use of a GIS-based hybrid artificial neural network to prioritize the order of pipe replacement in a water distribution network. Environmental Monitoring and Assessment, 166(1-4):177-189,
Kalin L, Isik S, Schoonover J E, Lockaby B G. 2010. Predicting water quality in unmonitored watersheds using artificial neural networks. Journal of Environmental Quality, 39(4):1 429-1 440,
Khudair B H, Jasim M M, Alsaqqar A S. 2018. Artificial neural network model for the prediction of groundwater quality. Civil Engineering Journal, 4(12):2 959-2 970,
Kia M B, Pirasteh S, Pradhan B, Mahmud A R, Sulaiman W N A, Moradi A. 2012. An artificial neural network model for flood simulation using GIS:Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1):251-264,
Lu F, Chen Z, Liu W Q, Shao H B. 2016. Modeling chlorophylla concentrations using an artificial neural network for precisely eco-restoring lake basin. Ecological Engineering, 95:422-429,
Lu F. 2015. Development of an Integrated GIS-Based System for Surface Water Quality Assessment and Management(GIS-SWQAM). Concordia University, Montreal.
Malekzadeh M, Kardar S, Shabanlou S. 2019. Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models. Groundwater for Sustainable Development, 9:100279,
Matouq M, El-Hasan T, Al-Bilbisi H, Abdelhadi M, Hindiyeh M, Eslamian S, Duheisat S. 2013. The climate change implication on Jordan:a case study using GIS and artificial neural networks for weather forecasting. Journal of Taibah University for Science, 7(2):44-55,
Moradzaeh A, Khaffafi K. 2017. Comparison and evaluation of the performance of various types of neural networks for planning issues related to optimal management of charging and discharging electric cars in intelligent power grids. Emerging Science Journal, 1(4):201-207,
Moriasi D N, Arnold J G, van Liew M W, Bingner R L, Harmel R D, Veith T L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3):885-900,
Nash J E, Sutcliffe J V. 1970. River flow forecasting through conceptual models part I-a discussion of principles. Journal of Hydrology, 10(3):282-290,
Noori N, Kalin L. 2016. Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology, 533:141-151,
Panda R K, Pramanik N, Bala B. 2010. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Computers & Geosciences, 36(6):735-745,
Pradhan P, Tingsanchali T, Shrestha S. 2019. Evaluation of soil and water assessment tool and artificial neural network models for hydrologic simulation in different climatic regions of Asia. Science of the Total Environment,
Ranković V, Radulović J, Radojević I, Ostojić A, Čomić L. 2010. Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia. Ecological Modelling, 221(8):1 239-1 244,
Saber A, James D E, Hayes D F. 2019. Estimation of water quality profiles in deep lakes based on easily measurable constituents at the water surface using artificial neural networks coupled with stationary wavelet transform. Science of the Total Environment, 694:133690,
Santini M, Caccamo G, Laurenti A, Noce S, Valentini R. 2010. A multi-component GIS framework for desertification risk assessment by an integrated index. Applied Geography, 30(3):394-415,
Sarkar A, Pandey P. 2015. River water quality modelling using artificial neural network technique. Aquatic Procedia, 4:1 070-1 077,
Singh A, Imtiyaz M, Isaac R K, Denis D M. 2012. Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India. Agricultural Water Management, 104:113-120,
Smeltzer E, Shambaugh A D, Stangel P. 2012. Environmental change in Lake Champlain revealed by long-term monitoring. Journal of Great Lakes Research, 38(S1):6-18,
Smeltzer E. 2017. Long-Term Water Quality and Biological Monitoring Project for Lake Champlain. VT Department of Environmental Conservation. FEMC. (access date:May 15, 2019).
Wang F, Wang X, Chen B, Zhao Y, Yang Z F. 2013. Chlorophyll a simulation in a lake ecosystem using a model with wavelet analysis and artificial neural network.
Environmental Management, 51(5):1 044-1 054,
Wu N C, Huang J C, Schmalz B, Fohrer N. 2014. Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches. Limnology, 15(1):47-56,
Yoo C, Kim J M. 2007. Tunneling performance prediction using an integrated GIS and neural network. Computers and Geotechnics, 34(1):19-30,
Zamanisabzi H, King J P, Dilekli N, Shoghli B, Abudu S. 2018. Developing an ANN based streamflow forecast model utilizing data-mining techniques to improve reservoir streamflow prediction accuracy:a case study. Civil Engineering Journal, 4(5):1 135-1 156,
Zhang Y Y, Gao X, Smith K, Inial G, Liu S M, Conil L B, Pan B C. 2019. Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164:114888,
Copyright © Haiyang Xuebao