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

Fang LU; Haoqing ZHANG; Wenquan LIU

doi:10.1007/s00343-019-9174-x

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

Ecology | Updated：2023-04-27

- Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area
- Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area
- 海洋湖沼学报（英文） 2020年38卷第6期页码：1835-1845
- Affiliations：
  
  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
- Author bio：
  
  LIU Wenquan, liuwq05@163.com
- Funds：
  
  the National Natural Science Foundation of China(41807247);the National Natural Science Foundation of China(41807229);the Special Fund for Shandong Post-doctoral Innovation Project;© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
- DOI：10.1007/s00343-019-9174-x
  中图分类号：
- 收稿：2019-07-10，
  
  录用：2019-9-16，
  
  网络首发：2019-10-24，
  
  纸质出版：2020-11
- Accepted：
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Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area[J]. 海洋湖沼学报（英文）, 2020,38(6):1835-1845.

Fang LU, Haoqing ZHANG, Wenquan LIU. 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[J]. 海洋湖沼学报（英文）, 2020,38(6):1835-1845. DOI： 10.1007/s00343-019-9174-x.

Fang LU, Haoqing ZHANG, Wenquan LIU. 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. DOI： 10.1007/s00343-019-9174-x.

摘要

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.

关键词

Keywords

references

W Al-Sabhan , M Mulligan , G A Blackburn . A real-time hydrological model for flood prediction using GIS and the WWW . Computers, Environment and Urban Systems , 2003 . 27 ( 1 ): 9 - 32 . DOI: 10.1016/S0198-9715(01)00010-2 http://doi.org/10.1016/S0198-9715(01)00010-2 DOI: 10.1016/S0198-9715(01)00010-2 http://doi.org/10.1016/S0198-9715(01)00010-2 .

S Azimi , Moghaddam M Azhdary , Monfared S A Hashemi . Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering . Journal of Contaminant Hydrology , 2019 . 220 6 - 17 . DOI: 10.1016/j.jconhyd.2018.10.010 http://doi.org/10.1016/j.jconhyd.2018.10.010 .

J E Brandmeyer , H A Karimi . Coupling methodologies for environmental models . Environmental Modelling & Software , 2000 . 15 ( 5 ): - 479 . - 488 . DOI: 10.1016/S1364-8152(00)00027-X http://doi.org/10.1016/S1364-8152(00)00027-X .

S Cho , B Lim , J Jung , S Kim , H Chae , J Park , S Park , J K Park . Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network . Measurement , 2014 . 53 224 - 233 . DOI: 10.1016/j.measurement.2014.03.044 http://doi.org/10.1016/j.measurement.2014.03.044 .

F Debaine , M Robin . A new GIS modelling of coastal dune protection services against physical coastal hazards . Ocean & Coastal Management , 2012 . 63 43 - 54 . DOI: 10.1016/j.ocecoaman.2012.03.012 http://doi.org/10.1016/j.ocecoaman.2012.03.012 .

O Deperlioglu , U Kose . An educational tool for artificial neural networks . Computers & Electrical Engineering , 2011 . 37 ( 3 ): 392 - 402 . DOI: 10.1016/j.compeleceng.2011.03.010 http://doi.org/10.1016/j.compeleceng.2011.03.010 .

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.

J García-Alba , J F Bárcena , C Ugarteburu , A García . Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries . Water Research , 2019 . 150 283 - 295 . DOI: 10.1016/j.watres.2018.11.063 http://doi.org/10.1016/j.watres.2018.11.063 .

L T Ghebremichael , T L Veith , M C Watzin . Determination of critical source areas for phosphorus loss: Lake Champlain basin, Vermont . Transactions of the ASABE , 2010 . 53 ( 5 ): 1 595 - 1 604 . DOI: 10.13031/2013.34898 http://doi.org/10.13031/2013.34898 .

C I Ho , M D Lin , S L Lo . 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 , 2010 . 166 ( 1-4 ): 177 - 189 . DOI: 10.1007/s10661-009-0994-6 http://doi.org/10.1007/s10661-009-0994-6 .

L Kalin , S Isik , J E Schoonover , B G Lockaby . Predicting water quality in unmonitored watersheds using artificial neural networks . Journal of Environmental Quality , 2010 . 39 ( 4 ): 1 429 - 1 440 . DOI: 10.2134/jeq2009.0441 http://doi.org/10.2134/jeq2009.0441 .

B H Khudair , M M Jasim , A S Alsaqqar . Artificial neural network model for the prediction of groundwater quality . Civil Engineering Journal , 2018 . 4 ( 12 ): 2 959 - 2 970 . DOI: 10.28991/cej-03091212 http://doi.org/10.28991/cej-03091212 .

M B Kia , S Pirasteh , B Pradhan , A R Mahmud , W N A Sulaiman , A Moradi . An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia . Environmental Earth Sciences , 2012 . 67 ( 1 ): 251 - 264 . DOI: 10.1007/s12665-011-1504-z http://doi.org/10.1007/s12665-011-1504-z .

F Lu , Z Chen , W Q Liu , H B Shao . Modeling chlorophyll- a concentrations using an artificial neural network for precisely eco-restoring lake basin . Ecological Engineering , 2016 . 95 422 - 429 . DOI: 10.1016/j.ecoleng.2016.06.072 http://doi.org/10.1016/j.ecoleng.2016.06.072 .

Lu F. 2015. Development of an Integrated GIS-Based System for Surface Water Quality Assessment and Management (GIS-SWQAM). Concordia University, Montreal.

M Malekzadeh , S Kardar , S Shabanlou . Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models . Groundwater for Sustainable Development , 2015 . 9 100279 DOI: 10.1016/j.gsd.2019.100279 http://doi.org/10.1016/j.gsd.2019.100279 .

M Matouq , T El-Hasan , H Al-Bilbisi , M Abdelhadi , M Hindiyeh , S Eslamian , S Duheisat . The climate change implication on Jordan: a case study using GIS and artificial neural networks for weather forecasting . Journal of Taibah University for Science , 2013 . 7 ( 2 ): 44 - 55 . DOI: 10.1016/j.jtusci.2013.04.001 http://doi.org/10.1016/j.jtusci.2013.04.001 .

A Moradzaeh , K Khaffafi . 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 , 2017 . 1 ( 4 ): 201 - 207 . DOI: 10.28991/ijse-01123 http://doi.org/10.28991/ijse-01123 .

D N Moriasi , J G Arnold , M W van Liew , R L Bingner , R D Harmel , T L Veith . Model evaluation guidelines for systematic quantification of accuracy in watershed simulations . Transactions of the ASABE , 2007 . 50 ( 3 ): 885 - 900 . DOI: 10.13031/2013.23153 http://doi.org/10.13031/2013.23153 .

J E Nash , J V Sutcliffe . River flow forecasting through conceptual models part I—a discussion of principles . Journal of Hydrology , 1970 . 10 ( 3 ): 282 - 290 . DOI: 10.1016/0022-1694(70)90255-6 http://doi.org/10.1016/0022-1694(70)90255-6 .

N Noori , L Kalin . Coupling SWAT and ANN models for enhanced daily streamflow prediction . Journal of Hydrology , 2016 . 533 141 - 151 . DOI: 10.1016/j.jhydrol.2015.11.050 http://doi.org/10.1016/j.jhydrol.2015.11.050 .

R K Panda , N Pramanik , B Bala . Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model . Computers & Geosciences , 2010 . 36 ( 6 ): 735 - 745 . DOI: 10.1016/j.cageo.2009.07.012 http://doi.org/10.1016/j.cageo.2009.07.012 .

P Pradhan , T Tingsanchali , S Shrestha . 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 , 2019 . DOI: 10.1016/j.scitotenv.2019.134308 http://doi.org/10.1016/j.scitotenv.2019.134308 .

V Ranković , J Radulović , I Radojević , A Ostojić , L Čomić . Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia . Ecological Modelling , 2010 . 221 ( 8 ): 1 239 - 1 244 . DOI: 10.1016/j.ecolmodel.2009.12.023 http://doi.org/10.1016/j.ecolmodel.2009.12.023 .

A Saber , D E James , D F Hayes . 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 , 2019 . 694 133690 DOI: 10.1016/j.scitotenv.2019.133690 http://doi.org/10.1016/j.scitotenv.2019.133690 .

M Santini , G Caccamo , A Laurenti , S Noce , R Valentini . A multi-component GIS framework for desertification risk assessment by an integrated index . Applied Geography , 2010 . 30 ( 3 ): 394 - 415 . DOI: 10.1016/j.apgeog.2009.11.003 http://doi.org/10.1016/j.apgeog.2009.11.003 .

A Sarkar , P Pandey . River water quality modelling using artificial neural network technique . Aquatic Procedia , 2015 . 4 1 070 - 1 077 . DOI: 10.1016/j.aqpro.2015.02.135 http://doi.org/10.1016/j.aqpro.2015.02.135 .

A Singh , M Imtiyaz , R K Isaac , D M Denis . 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 , 2012 . 104 113 - 120 . DOI: 10.1016/j.agwat.2011.12.005 http://doi.org/10.1016/j.agwat.2011.12.005 .

E Smeltzer , A D Shambaugh , P Stangel . Environmental change in Lake Champlain revealed by long-term monitoring . Journal of Great Lakes Research , 2012 . 38 ( S1 ): 6 - 18 . DOI: 10.1016/j.jglr.2012.01.002 http://doi.org/10.1016/j.jglr.2012.01.002 .

Smeltzer E. 2017. Long-Term Water Quality and Biological Monitoring Project for Lake Champlain. VT Department of Environmental Conservation. FEMC. https://www.uvm.edu/femc/data/archive/project/long-term-water-quality-biological-monitoring https://www.uvm.edu/femc/data/archive/project/long-term-water-quality-biological-monitoring (access date: May 15, 2019).

F Wang , X Wang , B Chen , Y Zhao , Z F Yang . Chlorophyll a simulation in a lake ecosystem using a model with wavelet analysis and artificial neural network . Environmental Management , 2013 . 51 ( 5 ): 1 044 - 1 054 . DOI: 10.1007/s00267-013-0029-5 http://doi.org/10.1007/s00267-013-0029-5 .

N C Wu , J C Huang , B Schmalz , N Fohrer . Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches . Limnology , 2014 . 15 ( 1 ): 47 - 56 . DOI: 10.1007/s10201-013-0412-1 http://doi.org/10.1007/s10201-013-0412-1 .

C Yoo , J M Kim . Tunneling performance prediction using an integrated GIS and neural network . Computers and Geotechnics , 2007 . 34 ( 1 ): 19 - 30 . DOI: 10.1016/j.compgeo.2006.08.007 http://doi.org/10.1016/j.compgeo.2006.08.007 .

H Zamanisabzi , J P King , N Dilekli , B Shoghli , S Abudu . Developing an ANN based streamflow forecast model utilizing data-mining techniques to improve reservoir streamflow prediction accuracy: a case study . Civil Engineering Journal , 2018 . 4 ( 5 ): 1 135 - 1 156 . DOI: 10.28991/cej-0309163 http://doi.org/10.28991/cej-0309163 .

Y Y Zhang , X Gao , K Smith , G Inial , S M Liu , L B Conil , B C Pan . Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network . Water Research , 2019 . 164 114888 DOI: 10.1016/j.watres.2019.114888 http://doi.org/10.1016/j.watres.2019.114888 .

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