大洪水
计算机科学
规范化(社会学)
人工神经网络
人工智能
特征工程
数据挖掘
深度学习
机器学习
特征(语言学)
接收机工作特性
地理
哲学
社会学
语言学
考古
人类学
作者
Zhice Fang,Yi Wang,Ling Peng,Haoyuan Hong
标识
DOI:10.1016/j.jhydrol.2020.125734
摘要
Identifying floods and producing flood susceptibility maps are crucial steps for decision-makers to prevent and manage disasters. Plenty of studies have used machine learning models to produce reliable susceptibility maps. Nevertheless, most research ignores the importance of developing appropriate feature engineering methods. In this study, we propose a local spatial sequential long short-term memory neural network (LSS-LSTM) for flood susceptibility prediction in Shangyou County, China. The three main contributions of this study are summarized below. First of all, it is a new perspective to use the deep learning technique of LSTM for flood susceptibility prediction. Second, we integrate an appropriate feature engineering method with LSTM to predict flood susceptibility. Third, we implement two optimization techniques of data augmentation and batch normalization to further improve the performance of the proposed method. The LSS-LSTM method can not only capture the attribution information of flood conditioning factors and the local spatial information of flood data, but also has powerful sequential modelling capabilities to deal with the spatial relationship of floods. The experimental results demonstrate that the LSS-LSTM method achieves satisfactory prediction performance (93.75% and 0.965) in terms of accuracy and area under the receiver operating characteristic (ROC) curve.
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