دورية أكاديمية
Estimation of the recharging rate of groundwater using random forest technique
| العنوان: | Estimation of the recharging rate of groundwater using random forest technique |
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| المؤلفون: | Parveen Sihag, Anastasia Angelaki, Barkha Chaplot |
| المصدر: | Applied Water Science, Vol 10, Iss 7, Pp 1-11 (2020) |
| بيانات النشر: | SpringerOpen, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Water supply for domestic and industrial purposes |
| مصطلحات موضوعية: | Recharging rate, Random forest, Gaussian process regression, M5P tree, Water supply for domestic and industrial purposes, TD201-500 |
| الوصف: | Abstract 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2190-5487 2190-5495 |
| Relation: | http://link.springer.com/article/10.1007/s13201-020-01267-3; https://doaj.org/toc/2190-5487; https://doaj.org/toc/2190-5495 |
| DOI: | 10.1007/s13201-020-01267-3 |
| URL الوصول: | https://doaj.org/article/76e18bc13a2040978008e25b72d384ed |
| رقم الأكسشن: | edsdoj.76e18bc13a2040978008e25b72d384ed |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 21905487 21905495 |
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| DOI: | 10.1007/s13201-020-01267-3 |