Identifying most influencing input parameters for predicting chloride concentration in groundwater using an ANN approach

Kassem, Youssef; Gökçekuş, Hüseyin; Maliha, Mahmoud R. M.

doi:10.1007/s12665-021-09541-6

Cited by 8 publications

(1 citation statement)

References 67 publications

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“…Various versions of ML models have been reported in the literature, such as artificial neural network (ANN) 41 – 45 , support vector machine (SVM) 46 – 48 , adaptive neuro-fuzzy inference system (ANFIS) 49 , 50 , ensemble ML models 38 , 51 , 52 , group method of data handling (GMDH) 53 , and Gaussian process scheme 54 . The significant limitations associated with predictive ML models (1) the need for adequate input variables to explain the target data that may not be available everywhere 55 , 56 , (2) the influence of well excessive pumping 57 , 58 , (3) the reliability of the learning process of the predictive model where essential hyperparameters are optimized 59 , 60 , (4) coupled ML models where a pre-processing technique was integrated for data time series decomposition 61 , 62 . The ML model was adopted based on the inspiration of developing a new hybrid model for the ANFIS model.…”

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confidence: 99%

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

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

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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), Mg2+ (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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