Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models

Abstract

Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales.

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

“…and Bagging [53]. Such ensemble models also have been used in many other fields, for example groundwater potential mapping [46,54], gully erosion distribution modeling [55], and flood prediction [56].…”

“…Recent advances in data processing techniques have shown that the performance of machine learning methods can be further improved, and their limitations alleviated, using ensemble learning methods [40][41][42][43][44], which were first introduced in the early 1990s [45,46]. Ensemble methods have outperformed single model methods in terms of accuracy and robustness [40].…”

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

“…In particular, ensemble and hybrid machine learning techniques have provided promising results and have been widely used around the world in recent years [17,18,22,52,56,98,99]. Their base classifiers have good predictive ability and have been successful in predicting landslide-prone areas.…”

“…The prediction accuracy of landslide susceptibility models depends on the geographical region, landslide conditioning factors (LCF), sample size, and on hyper parameter tuning [18,99]. As yet, there is no consensus as to which models are most appropriate for specific regions, hence it is necessary to use a variety of methods in each study area to determine the method with the highest predictive power.…”

Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest

“…Vulnerability assessments have been conducted for natural disasters other than earthquakes, including floods [22][23][24][25][26], landslides [27][28][29][30][31][32][33][34], gully erosion [35][36][37][38][39], and groundwater contamination [40][41][42][43][44]. Some studies have compared the performance of various methodologies, including probabilistic techniques such as frequency ratio (FR) models [22,27,43], statistical techniques such as LR-based models [22,27,28,32,34,38,[40][41][42][43], and machine learning algorithms such as decision tree (DT) [24,26,28,29,31,34,38,39,[42][43][44], random forest (RF) …”