A Novel Machine Learning Application: Water Quality Resilience Prediction Model

Imani, Maryam and Hasan, Md Mahmudul and Bittencourt, Luiz F. and McClymont, Kent and Kapelan, Zoran (2021) A Novel Machine Learning Application: Water Quality Resilience Prediction Model. Science of the Total Environment, 768. p. 144459. ISSN 1879-1026

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Official URL: https://doi.org/10.1016/j.scitotenv.2020.144459


Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems’ characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of influential parameters which can be challenging, time consuming and costly to compute. It may also require building detailed conceptual and/or physically process-based models that are difficult to build, calibrate and validate. This study utilises Artificial Neural Network (ANN) to develop a novel application to predict water quality resilience to simplify resilience evaluation. The Fuzzy Analytic Hierarchy Process method is used to rank water basins based on their level of resilience and to identify the ones that demand prompt restoration strategies. The commonly used ‘magnitude * duration of being in failure state’ quantification method has been used to formulate and evaluate resilience. A 17-years long water quality dataset from the 22 water basins in the State of São Paulo, Brazil, was used to train and test the ANN model. The overall agreement between the measured and simulated WQI resilience values is satisfactory and hence, can be used by planners and decision makers for improved water management. Moreover, comparative analyses show similarities and differences between the ‘level of criticalities’ reported in each zone by Environment Agency of the state of São Paulo (CETESB) and by the resilience model in this study.

Item Type: Journal Article
Keywords: Artificial neural network, analytic hierarchy process, fuzzy logic, triangular fuzzy number, machine learning, resilience, water quality
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 18 Jan 2021 10:35
Last Modified: 20 Jan 2021 11:33
URI: https://arro.anglia.ac.uk/id/eprint/706192

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