机译:基于小波的水文时间序列预测
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
Caroline and William N. Lehrer Distinguished Chair in Water Engineering, Dept. of Biological and Agricultural Engineering and Zachry Dept. of Civil Engineering, Texas A&M Univ., 321 Scoates Hall, 2117 TAMU, College Station, TX 77843-2117;
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China;
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
Hydrological forecasting; Artificial intelligence modeling; Wavelet analysis; Temporal scale; Hydrological time series analysis; Statistical significance;
机译:基于离散小波的水文时间序列趋势识别
机译:解决基于小波的水文和水资源资源预测模型的错误使用,具有最佳实践和新的预测框架
机译:基于小波神经网络和地球物理时间序列预测经典技术的比较研究
机译:基于小波的时间序列预测方法
机译:利用离散小波变换分解春季时间序列,以表征裂隙含水层,并利用人工神经网络进行水文预报。
机译:水文时间序列预测的四阶段混合模型
机译:基于小波的水文时间序列预测