Cuckoo Optimization Algorithm (COA); Groundwater quality; Machine learning; Multiverse Optimizer (MVO); Teaching Learning-Based Optimization (TLBO); Cuckoo optimization algorithm; Evaporation rate; Machine-learning; Multiverse optimizer; Optimization algorithms; Optimizers; Teaching learning-based optimization; Teaching-learning-based optimizations; Total hardness; Global and Planetary Change; Environmental Chemistry; Water Science and Technology; Soil Science; Pollution; Geology; Earth-Surface Processes
Abstract :
[en] A groundwater reservoir is either a solitary aquifer or a network of interconnected aquifers. A particular aquifer’s groundwater purity evaluation could be time-consuming and costly. This study quantified the properties of Na%, SO42−, Cl, Na+, Mg2+, Ca2+, HCO3−, K+, and pH to predict the water quality parameter known as total hardness (as CaCO3). Groundwater quality data for the Shiraz Plain from 2002 to 2018 was utilized to accomplish this objective. The paper contrasts a hybrid methodology that combines Teaching Learning-Based Optimization (TLBO), Multiverse Optimizer (MVO), the Cuckoo Optimization Algorithm (COA), and the Evaporation Rate-based Water Cycle Algorithm (ER-WCA) with Artificial Neural Networks (ANN) this was done to design an optimal network for groundwater quality with conventional ANN. In comparison to all other TLBO-ANN, MVO-ANN, and COA-ANN developed models, the ER-WCA-ANN technique (with a population size of 500 and eight neurons in each hidden layer) provided the most accurate prediction for the TH with R2 values of 0.9983 and 0.98261, and RMSE values of 0.03698 and 0.00611, respectively, in the training and testing datasets. A comparison of the findings for the forecasting of groundwater quality showed that the ER-WCA-ANN hybrid model might increase prediction accuracy. These findings may have significant implications for future groundwater quality assessments.
Disciplines :
Agriculture & agronomy
Author, co-author :
Moayedi, Hossein; Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam ; School of Engineering and Technology, Duy Tan University, Da Nang, Viet Nam
Salari, Marjan; Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
Ali, Sana Abdul-Jabbar; Pharmacy Department, AlSafwa University College, Karbalaa, Iraq
Dehrashid, Atefeh Ahmadi; Department of Climatology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement
Language :
English
Title :
Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models
Publication date :
July 2024
Journal title :
Environmental Earth Sciences
ISSN :
1866-6280
eISSN :
1866-6299
Publisher :
Springer Science and Business Media Deutschland GmbH
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