The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

Abstract

Machine learning models have attracted much research attention for groundwater potential mapping. However, the accuracy of models for groundwater potential mapping is significantly influenced by sample size and this is still a challenge. This study evaluates the influence of sample size on the accuracy of different individual and hybrid models, adaptive neuro-fuzzy inference system (ANFIS), ANFISimperial competitive algorithm (ANFIS-ICA), alternating decision tree (ADT), and random forest (RF) to model groundwater potential, considering the number of springs from 177 to 714. A well-documented inventory of springs, as a natural representative of groundwater potential, was used to designate four sample data sets: 100% (D1), 75% (D2), 50% (D3), and 25% (D4) of the entire springs inventory. Each data set was randomly split into two groups of 30% (for training) and 70% (for validation). Fifteen diverse geo-environmental factors were employed as independent variables. The area under the operating 2 receiver characteristic curve (AUROC) and the true skill statistic (TSS) as two cutoff-independent and cutoff-dependent performance metrics were used to assess the performance of models. Results showed that the sample size influenced the performance of four machine learning algorithms, but RF had a lower sensitivity to the reduction of sample size. In addition, validation results revealed that RF (AUROC=90.74-96.32%, TSS=0.79-0.85) had the best performance based on all four sample data sets, followed by ANFIS-ICA (AUROC=81.23-91.55%, TSS=0.74-0.81), ADT (AUROC=79.29-88.46%, TSS=0.59-0.74), and ANFIS (AUROC=73.11-88.43%, TSS=0.59-0.74). Further, the relative slope position, lithology, and distance from faults were the main spring-affecting factors contributing to groundwater potential modelling. This study can provide useful guidelines and valuable reference for selecting machine learning models when a complete spring inventory in a watershed is unavailable.

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

“…This is partly consistent with findings by Rahmati et al [20] and Naghibi et al [75], who concluded that RF is a powerful predictive model that can run accurately with large databases and can process thousands of input variables without variable deletion. Similarly, Davoudi Moghaddam et al [29] found that RF prepares estimates of the variables that are vital for classification, is robust with unbalanced datasets and missing data, and offers an experimental technique for detecting variable interactions [57]. In an earlier study in the Bojnourd Watershed, Iran, RF also demonstrated excellent performance in groundwater potential modeling [20] and similar findings have been made in some other areas [75,76].…”

“…In addition, credible knowledge of the relationships between predictor variables (e.g., geo-environmental factors, etc.) and the respective target variable (i.e., groundwater potential) can help decision-makers achieve sustainable groundwater management, but these associations are still somewhat complicated and poorly documented [29]. The results of variable importance analysis using the RF model indicated higher importance of RSP, lithology, TWI, altitude, and drainage density, which shows the significant impact of DEM-derived factors on groundwater potential modeling ( Table 3).…”

“…All 10 conditioning factors were converted to a raster grid with 20 × 20 m cells for application of the GARP, QUEST, and RF models. Slope factor has a very significant role in groundwater potential mapping, and therefore a slope percent map of the study area was prepared [29] (Figure 4a). RSP can be applied to identify topographic characteristics such as valleys, ridge tops, flat surfaces, mid-slopes, foot-slopes, and upper slopes, with the value for RSP ranging from 0 (flat surface and valleys) to 1 (upper-slopes and ridge tops) [39].…”

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

“…Rainfall is one of the most important factors for groundwater potential mapping as it directly affects groundwater recharge [10,12,22]. The yearly average rainfall of this area varies from 4.80 to 7.23 mm (Figure 2j).…”

“…Therefore, further research on the exploration and estimation of groundwater potential and resources is necessary for proper water management of an area. Among different management strategies, potential groundwater mapping is an effective approach that can assist managers to adopt more efficient management plans [8][9][10][11][12].…”