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Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

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Abstract

In the drought prone district of Dholpur in Rajasthan, India, groundwater is a lifeline for its inhabitants. With population explosion and rapid urbanization, the groundwater is being critically over-exploited. Hence the current groundwater potential mapping study was undertaken to ascertain the areas that are more likely to yield a larger volume of groundwater against those areas that have poor groundwater potential and accordingly perpetuate the much needed damage control. Thematic layers for 14 groundwater influencing factors were considered for the study region, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), geology, soil, land use, normalized difference vegetation index (NDVI), surface temperature, precipitation, distance from roads, and distance from rivers. These were then subjected to an overlay operation, with the groundwater inventory which comprised of the locations of observational groundwater wells. The resulting geospatial database was then used to train two decision tree based ensemble models: gradient boosted decision trees (GBDT) and random forest (RF). The predictive performance of these models was then compared using various performance metrics such as area under curve (AUC) of receiver operating characteristics (ROC), sensitivity, accuracy, etc. It was found that GBDT (AUC: 0.79) outperformed RF (AUC: 0.71). The validated GBDT model was then used to construct the groundwater potential zonation map. The generated map showed that about 20.2% of the region has very high potential, while 22.6% has high potential to yield groundwater, and approximately 19.9–17.5% of the study region has very low to low groundwater potential.

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  1. Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India

    Shruti Sachdeva & Bijendra Kumar

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  1. Shruti Sachdeva
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Appendix

Appendix

The performances of RF and GBDT models at various dataset splits of (50/50, 60/40, 70/30, 80/20, and 90/10) are summarized in Table 6.

Table 6 Performance statistics for various training–testing splits
Full size table

From Table 6, it can be observed that during testing, the best performance for both the models is delivered when 80% of the dataset were being used for training, i.e. developing the model, while the rest 20% of the dataset is used for testing purpose. Since a classifier’s true performance is gauged by how well the model performs on unseen data, hence the accuracy attained by the model at testing phase is taken as the true metric for determining the ratio for splitting the dataset. Hence, the ratio of 80:20 was employed for generating training and testing data sets.

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Sachdeva, S., Kumar, B. Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India. Stoch Environ Res Risk Assess 35, 287–306 (2021). https://doi.org/10.1007/s00477-020-01891-0

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