Abstract
Recently, extreme events have been occurring more frequently, a possible result of climate change, and have resulted in both significant economic losses as well as loss of life around the world [...]
Highlights
This Atmosphere Special Issue collected five original papers focused on research associated with the integration of advanced soft computing techniques in hydrological predictions
Han et al [8] of Xiamen University and the University of New South Wales presented three models, including a nonparametric k-nearest neighbor model, which employs a parameter selection method based on partial information coefficient to simulate the rainfall–runoff generation relationship in the Jiulong River catchment, China
Tayyab et al [9] of China Three Gorges University and the Huazhong University of Science and Technology developed a novel hybrid artificial neural networks model based on ensemble empirical mode decomposition and discrete wavelet transform to predict riverflow, validating its efficiency at the Upper Indus Basin
Summary
This Atmosphere Special Issue collected five original papers focused on research associated with the integration of advanced soft computing techniques in hydrological predictions. Han et al [8] of Xiamen University and the University of New South Wales presented three models, including a nonparametric k-nearest neighbor model, which employs a parameter selection method based on partial information coefficient to simulate the rainfall–runoff generation relationship in the Jiulong River catchment, China.
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