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Ensemble learning of daily river discharge modeling for two watersheds with different climates
Atmospheric Science Letters ( IF 2.0 ) Pub Date : 2020-06-08 , DOI: 10.1002/asl.1000 Jingwen Xu 1 , Qun Zhang 2 , Shuang Liu 3 , Shaojie Zhang 3 , Shengjie Jin 2 , Dongyu Li 1 , Xiaobo Wu 1 , Xiaojing Liu 1 , Ting Li 1 , Hao Li 1
Atmospheric Science Letters ( IF 2.0 ) Pub Date : 2020-06-08 , DOI: 10.1002/asl.1000 Jingwen Xu 1 , Qun Zhang 2 , Shuang Liu 3 , Shaojie Zhang 3 , Shengjie Jin 2 , Dongyu Li 1 , Xiaobo Wu 1 , Xiaojing Liu 1 , Ting Li 1 , Hao Li 1
Affiliation
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In order to reduce the uncertainties and improve the river discharge modeling accuracy, several topography‐based hydrological models (TOPMODEL), generated by different combinations of parameters, were incorporated into an ensemble learning framework with the boosting method. Both the Baohe River Basin (BRB) with humid climate, and the Linyi River Basin (LRB) with semi‐arid climate were chosen for model testing. Observed daily precipitation, pan evaporation and stream flow data were used for model development and testing. Different Nash‐Sutcliffe efficiency coefficients, the coefficient of determination and the Root Mean Square Error were adopted to implement a comprehensive assessment on model performances. Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. During the validation periods, the boosting method could increase the modeling accuracy by 9 and 16% for BRB and LRB, respectively. The ensemble method significantly narrowed the gap of model performances over watersheds with different climatic conditions. Hence, using the ensemble learning to enhance the feasibility of hydrological models for different climatic regions is promising.
中文翻译：
集合学习两个气候不同的流域的每日河流流量模型
为了减少不确定性并提高河流流量建模的准确性，使用boosting方法将由不同参数组合生成的几种基于地形的水文模型（TOPMODEL）合并到集成学习框架中。模型测试选择了潮湿气候的宝河流域（BRB）和半干旱气候的临沂流域（LRB）。观察到的每日降水，蒸发皿蒸发和水流数据被用于模型开发和测试。采用不同的Nash-Sutcliffe效率系数，确定系数和均方根误差对模型性能进行全面评估。测试结果表明，集成学习方法与最佳的单个TOPMODEL相比可以提高建模精度。在验证期间，对于BRB和LRB，boosting方法可使建模精度分别提高9％和16％。集成方法大大缩小了不同气候条件下流域模型性能的差距。因此，利用集成学习来提高针对不同气候区域的水文模型的可行性是有希望的。
更新日期：2020-06-08
中文翻译：
集合学习两个气候不同的流域的每日河流流量模型
为了减少不确定性并提高河流流量建模的准确性，使用boosting方法将由不同参数组合生成的几种基于地形的水文模型（TOPMODEL）合并到集成学习框架中。模型测试选择了潮湿气候的宝河流域（BRB）和半干旱气候的临沂流域（LRB）。观察到的每日降水，蒸发皿蒸发和水流数据被用于模型开发和测试。采用不同的Nash-Sutcliffe效率系数，确定系数和均方根误差对模型性能进行全面评估。测试结果表明，集成学习方法与最佳的单个TOPMODEL相比可以提高建模精度。在验证期间，对于BRB和LRB，boosting方法可使建模精度分别提高9％和16％。集成方法大大缩小了不同气候条件下流域模型性能的差距。因此，利用集成学习来提高针对不同气候区域的水文模型的可行性是有希望的。
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