A novel machine learning application: Water quality resilience prediction Model

Imani, Maryam; Hasan, Md Mahmudul; Bittencourt, Luiz Fernando; McClymont, Kent; Kapelan, Z.

doi:10.1016/j.scitotenv.2020.144459

A novel machine learning application

Title

A novel machine learning application: Water quality resilience prediction Model

Author

Imani, Maryam (Anglia Ruskin University)
Hasan, Md Mahmudul (Anglia Ruskin University)
Bittencourt, Luiz Fernando (University of Campinas)
McClymont, Kent (Anglia Ruskin University)
Kapelan, Z. (TU Delft Sanitary Engineering)

Date

2021

Abstract

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.

Subject

Analytic hierarchy process
Artificial neural network
Fuzzy logic
Machine learning
Resilience
Triangular fuzzy number
Water quality

To reference this document use:

http://resolver.tudelft.nl/uuid:5f9b2836-9aa9-44a5-8017-124b2793f7f3

DOI

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

Embargo date

2022-01-14

ISSN

0048-9697

Source

Science of the Total Environment, 768