Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
Abstract Using daily rainfall data from 1936 stations across China, this study investigated tropical cyclonic rainfall (TCR) changes during 1960–2014. The possible reasons behind TCR changes were examined using tracks and frequency of tropical cyclones (TCs) in both space and time. The highest annual TCR occur in coastal regions of east and southeast China (>500 mm/year). At monthly scale, August TCR can reach 150–250 mm in coastal regions. From the contribution viewpoint, TCR accounts for more than 40% of the monthly total rainfall and extreme rainfall events along the southeast coast of China. The contributions of TCR to the monthly rainfall amount decrease rapidly from coast to inland and are even faster for contributions of TCR to extreme rainfall. The distance inland from the shoreline with 250 km has been identified as the threshold, within that these contributions abruptly increase with decreasing distance from shoreline, and vice versa. In terms of extreme rainfall regimes, logistic and Poisson regressive techniques were used to identify the connections between TC‐induced extreme rainfall and El Niño–Southern Oscillation. Both these two regressions reveal that TC‐induced extreme rainfall tends to occur with higher frequency and magnitude in southeastern China (east and northeast coast of China) during La Niña (El Niño) years (El Niño). These consistent relations and remarkable spatial patterns can help to predict the occurrence of TC‐induced extreme rainfall events across eastern China.
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