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Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria; Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Department of Electrical Engineering, Kano University of Science and Technology, Wudil, Nigeria.
Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq.ORCID iD: 0000-0002-1708-0612
Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska street 60, 41-200, Sosnowiec, Poland.
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2022 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 114, article id 108036Article in journal (Refereed) Published
Abstract [en]

The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 114, article id 108036
Keywords [en]
Artificial Intelligence, Backpropagation neural network, Extreme gradient boosting, Genetic algorithm, Multilinear regression
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-87861DOI: 10.1016/j.asoc.2021.108036ISI: 000736985700004Scopus ID: 2-s2.0-85119691155OAI: oai:DiVA.org:ltu-87861DiVA, id: diva2:1610532
Note

Validerad;2022;Nivå 2;2022-01-11 (johcin);

Funder: Kano State Government

Available from: 2021-11-11 Created: 2021-11-11 Last updated: 2023-09-05Bibliographically approved

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