Accurate knowledge of the recharging rate is essential for several groundwater-related studies and projects mainly in the water scarcity regions. In this study, a comparison between different methods of soft computing-based models was obtained in order to evaluate and select the most suitable and accurate method for predicting the recharging rate of groundwater, as the natural recharging rate of the groundwater is important in efficient groundwater resource management and aquifer recharge. Experimental data have been used to investigate the improved performance of Gaussian process (GP), M5P and random forest (RF)-based regression method and evaluate the potential of these techniques in the prediction of natural recharging rate. The study also compares the prediction of recharging rate to empirical (Kostiakov model, multilinear regression, multi-nonlinear regression) equations. The RF method was selected for the recharging rate prediction and was compared with the M5P tree, GP and also empirical models. While GP, M5P tree and empirical models provide good quality of prediction performance, RF model showed superiority among them with coefficient of correlation (R) values as 0.98 and 0.91 for training and testing, respectively. Out of 106 observations collected from laboratory experiments, 73 were used for developing different models, whereas rest 33 observations were used for the assessment of the models’ performance. Sensitivity analysis recommends that time parameter (t) is the main influencing parameter, which is crucial for the prediction of the recharging rate. RF-based model is suitable for accurate prediction of recharging rate of groundwater.

中文翻译：

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利用随机森林技术估算地下水的补给率

对补给率的准确了解对于一些主要在缺水地区的与地下水有关的研究和项目至关重要。在这项研究中，对基于软计算的模型的不同方法进行了比较，以便评估和选择最合适，最准确的预测地下水补给率的方法，因为地下水的自然补给率对有效的地下水很重要。资源管理和含水层补给。实验数据已用于研究基于高斯过程（GP），M5P和基于随机森林（RF）的回归方法的改进性能，并评估了这些技术在预测自然充电率方面的潜力。研究还将充电率的预测与经验（Kostiakov模型，多元线性回归，多非线性回归）方程。选择了RF方法进行充电率预测，并将其与M5P树，GP和经验模型进行了比较。虽然GP，M5P树和经验模型提供了良好的预测性能，但RF模型显示了它们之间的相关系数优势（对于训练和测试，R）值分别为0.98和0.91。从实验室实验收集的106个观察结果中，有73个被用于开发不同的模型，而其余的33个观察则被用于评估模型的性能。灵敏度分析建议，时间参数（t）是主要的影响参数，这对于预测充电率至关重要。基于RF的模型适合于准确预测地下水的补给率。