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Jian Chen, Yaowei Li and Shanju Zhang
Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people?s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machi...
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Li He, Shasha Ji, Kunlun Xin, Zewei Chen, Lei Chen, Jun Nan and Chenxi Song
Hydraulic monitoring data is critical for optimizing drainage system design and predicting system performance, particularly in the establishment of data-driven hydraulic models. However, anomalies in monitoring data, caused by sensor failures and network...
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Shiva Gopal Shrestha and Soni M. Pradhanang
The general practice of rainfall-runoff model development towards physically based and spatially explicit representations of hydrological processes is data-intensive and computationally expensive. Physically based models such as the Soil Water Assessment...
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Seng Choon Toh, Sai Hin Lai, Majid Mirzaei, Eugene Zhen Xiang Soo and Fang Yenn Teo
This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) ...
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Jeonghyeon Choi, Jeongeun Won, Suhyung Jang and Sangdan Kim
Many studies have applied the Long Short-Term Memory (LSTM), one of the Recurrent Neural Networks (RNNs), to rainfall-runoff modeling. These data-driven modeling approaches learn the patterns observed from input and output data. It is widely known that t...
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