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Watershed Planning within a Quantitative Scenario Analysis Framework
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A hybrid evolutionary data driven model for river water quality early warning.

Alejandra Burchard-Levine1, Shuming Liu2, Francois Vince3

  • 1Tsinghua University - Veolia Environnement Joint Research Center for Advanced Environmental Technology, School of Environment, Tsinghua University, Beijing, China.

Journal of Environmental Management
|May 17, 2014
PubMed
Summary

China

Keywords:
Artificial Neural NetworksChinaEarly warning systemGenetic algorithmWater quality

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Area of Science:

  • Environmental Science
  • Water Quality Management
  • Data Science in Environmental Monitoring

Background:

  • Rapid industrialization and population growth in China have led to increased surface water pollution incidents.
  • Following the 2005 Songhua River incident, China has prioritized developing early warning systems (EWS) for drinking water protection.
  • Existing EWS in China face challenges including insufficient pollution monitoring and advanced water quality prediction models.

Purpose of the Study:

  • To evaluate the effectiveness of a Data Driven Model (DDM) using a Genetic Algorithm (GA) and Artificial Neural Network (ANN) for enhancing drinking water source protection.
  • To improve the response time of early warning systems in a South Chinese industrial city by predicting key water quality parameters.
  • To identify sensitive upstream (Station A) input variables for predicting downstream (Station B) water quality.

Main Methods:

  • A hybrid GA-ANN model was developed and applied to predict NH3-N, Chemical Oxygen Demand (CODmn), and Total Organic Carbon (TOC) at Station B.
  • The model utilized upstream data from Station A, located 12 km away, to identify sensitive input variables.
  • Model performance was assessed using Mean Square Error (MSE), Mean Percent Error (MPE), and regression (R) values.

Main Results:

  • The GA-ANN model successfully predicted NH3-N, CODmn, and TOC with high accuracy (R values of 92%, 87%, and 94%, respectively).
  • Sensitive upstream predictors for NH3-N included TOC, CODmn, Total Phosphorus (TP), NH3-N, and Turbidity.
  • Turbidity and CODmn were identified as the most sensitive predictors for both COD and TOC.
  • The model demonstrated improved predictive performance for an 8-hour ahead forecast.

Conclusions:

  • The GA-ANN model offers a viable and effective alternative to traditional physical models for water quality prediction in China.
  • This data-driven approach can significantly enhance the response time and accuracy of early warning systems for drinking water sources.
  • Future implementation in Chinese monitoring stations with existing water quality data can bolster water resource management and public safety.