Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling

Phong Tung Nguyen,Lanh Si Ho,Nadhir Al-Ansari,Duong Hai Ha,Hiep Van Le,Binh Thai Pham,Phan Trong Trinh,Indra Prakash,Huu Duy Nguyen,Tran Van Phong

doi:10.3390/su12072622

Abstract

Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.

Highlights

Groundwater is a vital natural resource for drinking water supply, irrigation and industries in many countries [1,2,3]
A new advanced method is built to measure quantitatively the total uncertainty from credal set based on the theory of Dempster and Shafer, as presented in following equation: TU(x) = IG(x) + GG(x) where x is defined as a credal set on frame X, TU is the total uncertainty value, IG is defined as a general function of non-specificity on the corresponding set of credits and GG is defined as a general randomness function for a credal set [32]
The results indicate that the BCDT model is the best in terms of training data (Figure 7), whereas the RSSCDT model is more efficient for the validation data in comparison to other models (Figure 8)

Summary

Introduction

Groundwater is a vital natural resource for drinking water supply, irrigation and industries in many countries [1,2,3]. Population growth creates higher demand for water for domestic use, in addition to industrial development and extension of irrigated areas [8,9]. This problem is more prevalent in the arid and semi-arid regions, which have faced numerous drought events in recent years due to erratic scanty rainfall [10,11]. Expert’s opinion-based models or weighted models have been used for groundwater potential mapping. These approaches are considered subjective and uncertainty [16,17]

Methods

Results

Conclusion