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Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe gro...

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Autores principales: Jamei, Mehdi, Karbasi, Masoud, Malik, Anurag, Abualigah, Laith, Islam, Abu Reza Md Towfiqul, Yaseen, Zaher Mundher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249886/
https://www.ncbi.nlm.nih.gov/pubmed/35778436
http://dx.doi.org/10.1038/s41598-022-15104-x
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author Jamei, Mehdi
Karbasi, Masoud
Malik, Anurag
Abualigah, Laith
Islam, Abu Reza Md Towfiqul
Yaseen, Zaher Mundher
author_facet Jamei, Mehdi
Karbasi, Masoud
Malik, Anurag
Abualigah, Laith
Islam, Abu Reza Md Towfiqul
Yaseen, Zaher Mundher
author_sort Jamei, Mehdi
collection PubMed
description The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl(−) (mg/l), Mg(2+) (mg/l), Na(+) (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.
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spelling pubmed-92498862022-07-03 Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh Jamei, Mehdi Karbasi, Masoud Malik, Anurag Abualigah, Laith Islam, Abu Reza Md Towfiqul Yaseen, Zaher Mundher Sci Rep Article The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl(−) (mg/l), Mg(2+) (mg/l), Na(+) (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers. Nature Publishing Group UK 2022-07-01 /pmc/articles/PMC9249886/ /pubmed/35778436 http://dx.doi.org/10.1038/s41598-022-15104-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jamei, Mehdi
Karbasi, Masoud
Malik, Anurag
Abualigah, Laith
Islam, Abu Reza Md Towfiqul
Yaseen, Zaher Mundher
Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
title Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
title_full Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
title_fullStr Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
title_full_unstemmed Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
title_short Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
title_sort computational assessment of groundwater salinity distribution within coastal multi-aquifers of bangladesh
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249886/
https://www.ncbi.nlm.nih.gov/pubmed/35778436
http://dx.doi.org/10.1038/s41598-022-15104-x
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