Efficiency enhancement of a process-based rainfall–runoff model using a new modified AdaBoost.RT technique |
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| Affiliation: | 1. College of Resources and Environment, Sichuan Agricultural University, Chengdu 611130, PR China;2. Chinese Academy of Meteorological Sciences, Beijing 100081, PR China;3. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, PR China;1. Institute of Geography, Heidelberg University, Heidelberg 69120, Germany;2. Research Station of Dongjiangyuan Forest Ecosystem, Guangdong Academy of Forestry, Guangzhou 510520, China;3. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;1. School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia;2. Department of Environment, Water and Natural Resources, GPO Box 2384, Adelaide, SA 5001, Australia;1. Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;2. College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;4. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;5. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China;1. Natural Geography Dept., University of Tehran, P.O. Box 14155-6465, Tehran, Iran;2. Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran |
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| Abstract: | High-efficiency rainfall–runoff forecast is extremely important for flood disaster warning. Single process-based rainfall–runoff model can hardly capture all the runoff characteristics, especially for flood periods and dry periods. In order to address the issue, an effective multi-model ensemble approach is urgently required. The Adaptive Boosting (AdaBoost) algorithm is one of the most robust ensemble learning methods. However, it has never been utilized for the efficiency improvement of process-based rainfall–runoff models.Therefore AdaBoost.RT (Adaptive Boosting for Regression problems and “T” for a threshold demarcating the correct from the incorrect) algorithm, is innovatively proposed to make an aggregation (AdaBoost-XXT) of a process-based rainfall–runoff model called XXT (a hybrid of TOPMODEL and Xinanjing model). To adapt to hydrologic situation, some modifications were made in AdaBoost.RT. Firstly, weights of wrong predicted examples were made increased rather than unchangeable so that those “hard” samples could be highlighted. Then the stationary threshold to demarcate the correct from the incorrect was replaced with dynamic mean value of absolute errors. In addition, other two minor modifications were also made. Then particle swarm optimization (PSO) was employed to determine the model parameters. Finally, the applicability of AdaBoost-XXT was tested in Linyi watershed with large-scale and semi-arid conditions and in Youshuijie catchment with small-scale area and humid climate. The results show that modified AdaBoost.RT algorithm significantly improves the performance of XXT in daily runoff prediction, especially for the large-scale watershed or low runoff periods, in terms of Nash–Sutcliffe efficiency coefficients and coefficients of determination. Furthermore, the AdaBoost-XXT has the more satisfactory generalization ability in processing input data, especially in Linyi watershed. Thus the method of using this modified AdaBoost.RT to enhance model performance is promising and easily extended to other process-based rainfall–runoff models. |
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| Keywords: | AdaBoost RT algorithm Particle swarm optimization Process-based hydrologic model |
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