Novel hybrid models to enhance the efficiency of groundwater potentiality model

Swapan Talukdar,Javed Mallick,Bushra Praveen,Abu Reza Md Towfiqul Islam,Atiqur Rahman,Showmitra Kumar Sarkar,Sujit Kumar Roy,Mohoua Sobnam,Mohd Waseem Naikoo

doi:10.1007/s13201-022-01571-0

Abstract

The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.

Highlights

In all climatic areas across the globe, groundwater is a highly significant and stable water source
We combined the fuzzy logic model with previously utilized ensemble machine learning (EML) to increase the accuracy of GPMs
The results show that the variables have no collinearity among themselves; we can use them for modeling groundwater potentiality models (GWP)

Summary

Introduction

In all climatic areas across the globe, groundwater is a highly significant and stable water source. The agriculture for developing countries like Bangladesh relies on irrigation based on groundwater. In Bangladesh, Groundwater provides around 79 percent of the water supply (Shahinuzzaman et al 2021). Groundwater provides 95 percent of irrigation supplies in certain sections, like the northwest (Shahinuzzaman et al 2021). Agriculture accounts for around 18 percent of Bangladesh’s GDP and provides jobs to about 48 percent of the workforce (Shahinuzzaman et al 2021). The development of groundwater resources is critical to the country's social and economic growth. It is critical for the agricultural policy of the government toward attaining food independence and poverty reduction (Salem et al 2017)

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Results

Discussion

Conclusion