首页|Data from Chinese Academy of Sciences Update Knowledge in Machine Learning (Enha ncing the streamflow simulation of a processbased hydrological model using mach ine learning and multi-source data)
Data from Chinese Academy of Sciences Update Knowledge in Machine Learning (Enha ncing the streamflow simulation of a processbased hydrological model using mach ine learning and multi-source data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on artificial intelligence have been published. According to news originating from Lanzhou, People’s Republ ic of China, by NewsRx editors, the research stated, “Streamflow simulation is c rucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mains tream tools for streamflow simulation.” The news correspondents obtained a quote from the research from Chinese Academy of Sciences: “However, their inherent limitations, such as time-consuming and la rge data requirements, make achieving high-precision simulations challenging. Th is study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise u se of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i. e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming cali bration algorithms to reduce computational costs. Then, XGBoost combines BTOP si mulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inpu ts and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jiali ng River basin, China. The results show that the performance of BTOP_ XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_ XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21% , and 83%, respectively. Meanwhile, the computational time of BTOP_ XGB is saved by >90% compared to the origi nal calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model.”
Chinese Academy of SciencesLanzhouPe ople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMach ine Learning
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