4.7 Article

Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations

期刊

WATER RESOURCES MANAGEMENT
卷 36, 期 7, 页码 2201-2221

出版社

SPRINGER
DOI: 10.1007/s11269-022-03136-x

关键词

Flood management; Data-driven models; Daily discharge; Burhabalang river

向作者/读者索取更多资源

Accurate and reliable discharge estimation is vital in managing water resources, agriculture, industry, and flood management. This study compares the performance of five data-driven tree-based algorithms in measuring the daily discharge of Govindpur site at Burhabalang river, India. The results show that the Random Tree model (Model-3) outperforms other models and can be used as a robust model for sustainable flood plain management.
Accurate and reliable discharge estimation is considered vital in managing water resources, agriculture, industry, and flood management on the basin scale. In this study, five data-driven tree-based algorithms: M5-Pruned model-M5P (Model-1), Random Forest-RF (Model-2), Random Tree-RT (Model-3), Reduced Error Pruning Tree-REP Tree (Model-4), and Decision Stump-DS (Model-5) have been examined to measure the daily discharge of Govindpur site at Burhabalang river, India. The proposed models will be calibrated by daily 10-years time-series hydrological data (i.e., river stage (h) and daily discharge (Q)) measured from 2004 to 2013. In these models, 70% and 30% of the dataset were used for the training and testing stage for the reliability of the developed models. The precision of the models was optimized by investigating five different scenarios based on various time-lags combinations. Model's performance has been assessed and evaluated using five statistical metrics, namely, correlation coefficient (R-2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). Results showed that Model-3 outperforms as compared to other proposed models. Machine learning models have been examined five scenarios of input variables during training and testing phases. In comparison of the Model-5 struggled in capturing the river's flow rate and showed poor performance in scenarios where R-2 metric values ranged from 0.64 to 0.94. Therefore, it can be concluded that the RT model could be used as a robust model for sustainable flood plain management.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据