Assessment of Groundwater Potential Based on Multicriteria Decision Making Model and Decision Tree Algorithms

Abstract

Groundwater plays an important role in global climate change and satisfying human needs. In the study, RS (remote sensing) and GIS (geographic information system) were utilized to generate five thematic layers, lithology, lineament density, topology, slope, and river density considered as factors influencing the groundwater potential. Then, the multicriteria decision model (MCDM) was integrated with C5.0 and CART, respectively, to generate the decision tree with 80 surveyed tube wells divided into four classes on the basis of the yield. To test the precision of the decision tree algorithms, the 10-fold cross validation and kappa coefficient were adopted and the average kappa coefficient for C5.0 and CART was 90.45% and 85.09%, respectively. After applying the decision tree to the whole study area, four classes of groundwater potential zones were demarcated. According to the classification result, the four grades of groundwater potential zones, "very good," "good," "moderate," and "poor," occupy 4.61%, 8.58%, 26.59%, and 60.23%, respectively, with C5.0 algorithm, while occupying the percentages of 4.68%, 10.09%, 26.10%, and 59.13%, respectively, with CART algorithm. Therefore, we can draw the conclusion that C5.0 algorithm is more appropriate than CART for the groundwater potential zone prediction.

A groundwater potential zone mapping approach for semi-arid environments using remote sensing (RS), geographic information system (GIS), and analytical hierarchical process (AHP) techniques: a case study of Buffalo catchment, Eastern Cape, South Africa

“…Due to the huge dataset that machine learning works with, decision-making is based on probability, whereas in MCDM statistics, the variance and uncertainty of statistical operands are concealed by the results (Mitchell 1997). Machine learning does not require the definition of sample distribution and does not provide any room for assumption meanwhile MCDM statistics require the definition of distribution and enables assumption, thereby, providing the chance to hypothesize (Duan et al 2016). As a result of the huge dataset, the outcome of machine learning can only be generalized since it works on probability and best fit, while the outcome of MCDM statistics can be fit to the defined data distribution (Kelleher et al 2015).…”

Remote Sensing: An Advanced Technique for Crop Condition Assessment

“…Decision trees, conversely, employ dichotomization to route available data to the precise group, as is observed in vegetal basics. Even if the distinction conditions at each stage of the decision tree arrangement are produced by the software, it is probable to examine the conditions in an effort to comprehend what origin distinction has been completed [68,69].…”

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

“…In recent years, machine learning algorithms (MLAs) have been proposed and suggested to solve many real world problems, including groundwater spring potential mapping, which are logistic regression (LR) [22,37,44], random forest [12,14,42], Naive Bayes [45], and decision tree (DT) [46][47][48]. However, they are also widely used in some fields of hydrology worldwide, including (i) surface water hydrology, such as rainfall and runoff forecasting [49,50], stream flow and sediment yield forecasting [51,52], evaporation and evapotranspiration forecasting [53][54][55], lake and reservoir water level prediction [56,57], flood susceptibility mapping and forecasting [58][59][60][61][62], and snow avalanche forecasting [63]; (ii) groundwater hydrology, such as groundwater level prediction [64], soil moisture estimation [65], and groundwater quality assessment [66].…”