Deep learning, flood simulation, multimodal data fusion, time series forecast, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

Flood disaster is a typical natural disaster that causes human casualties and property losses every year. Benefiting from powerful feature abstraction capabilities and automatic tuning characteristics, deep learning has become a powerful tool for disaster prediction. Nonetheless, many existing methods are developed for natural images and do not take into account the unique characteristics of remote-sensing images and other modal data. Furthermore, many methods are too complex to poor computational efficiency and interpretability. To this end, we proposed a multimodal based selective receptive field neural network (SRFNet). It fully adopts convolutional neural networks, which are simpler and more efficient compared to other state-of-the-art methods. It also incorporates selectively large kernel convolution for multiscale analysis of remote sensing images. In addition, the modal of rainfall and water level are also fully considered and exploited in the method to improve its performance. To verify the effectiveness and robustness of SRFNet, extensive and detailed experiments on the Dongting Lake and the Poyang Lake with the data range from year of 2010 to 2020 are conducted. As a result, our method outperforms the other seven state-of-the-art methods and can stably achieve structural similarity of more than 0.9 in multiple resolutions.