Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant-Reference-Cited by-同舟云学术

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Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant

Published:2024 Issue:1 Volume:3 Page:132-144
ISSN:2754-7000
Container-title:Environmental Science: Advances
language:en
Short-container-title:Environ. Sci.: Adv.

Author:

Hamada Mazen S.¹,Zaqoot Hossam Adel²^ORCID,Sethar Waqar Ahmed³

Affiliation:

1. Department of Chemistry, Faculty of Science, Al Azhar University, Gaza Strip, Palestine

2. Environment Quality Authority (Palestinian Authority), Gaza Strip, Palestine

3. Mehran University Institute of Science, Technology and Development, Mehran University of Engineering and Technology, Jamshoro, Pakistan

Abstract

This paper presents the use of a machine learning approach to predict the performance of a Gaza wastewater treatment plant.

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2024/VA/D3VA00170A

Reference53 articles.

1. Water, Sanitation, and Hygiene Vulnerability among Rural Areas and Small Towns in South Africa: Exploring the Role of Climate Change, Marginalization, and Inequality

2. Water Resources and Governance Approaches: Insights for Achieving Water Security

3. Science and technology for water purification in the coming decades

4. Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

5. Adaptive Levenberg-Marquardt Algorithm Based Echo State Network for Chaotic Time Series Prediction

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