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Watershed Planning within a Quantitative Scenario Analysis Framework
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A novel machine learning application: Water quality resilience prediction Model.

Maryam Imani1, Md Mahmudul Hasan2, Luiz Fernando Bittencourt3

  • 1School of Engineering & the Built Environment, Faculty of Science and Engineering, Anglia Ruskin University, Chelmsford, Essex CM1 1SQ, United Kingdom.

The Science of the Total Environment
|January 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an Artificial Neural Network (ANN) model to simplify water quality resilience assessment. The novel approach aids planners in identifying critical water basins for effective restoration strategies.

Keywords:
Analytic hierarchy processArtificial neural networkFuzzy logicMachine learningResilienceTriangular fuzzy numberWater quality

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence in Environmental Monitoring

Background:

  • Traditional water quality resilience assessment is complex, time-consuming, and costly.
  • Developing detailed physical or conceptual models for resilience evaluation presents significant challenges in calibration and validation.
  • Existing methods for quantifying resilience can be computationally intensive and difficult to implement.

Purpose of the Study:

  • To develop a novel application using Artificial Neural Networks (ANN) for predicting water quality resilience.
  • To simplify the complex process of evaluating water quality resilience.
  • To identify water basins requiring prompt restoration strategies through resilience ranking.

Main Methods:

  • Utilized Artificial Neural Network (ANN) for predicting water quality resilience.
  • Employed the Fuzzy Analytic Hierarchy Process (FAHP) for ranking water basins based on resilience.
  • Applied the 'magnitude * duration of being in failure state' method for resilience quantification.
  • Trained and tested the ANN model using a 17-year water quality dataset from 22 basins in São Paulo, Brazil.

Main Results:

  • The ANN model demonstrated satisfactory agreement between measured and simulated Water Quality Index (WQI) resilience values.
  • The Fuzzy Analytic Hierarchy Process effectively ranked water basins according to their resilience levels.
  • Comparative analysis revealed similarities and differences between the study's resilience model and the state's environmental agency (CETESB) criticality assessments.

Conclusions:

  • The developed ANN model offers a simplified and effective tool for predicting water quality resilience.
  • The findings support the use of this model by planners and decision-makers for improved water resource management.
  • The study provides valuable insights into water basin criticalities, aiding targeted intervention strategies.