Precipitation is a critical input for hydrologic simulation and prediction, and is widely used for agriculture, water resources management, and prediction of flood and drought, among other activities. Traditional precipitation prediction researches often established one or more probability models of historical data based on the statistical prediction methods and machine learning techniques. However, few studies have been attempted deep learning methods such as the state-of-the-art for Recurrent Neural Networks (RNNs) networks in meteorological sequence time series predictions. We deployed Long Short-Term Memory (LSTM) network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City. After identifying the correlation between meteorological variables and the precipitation, nine significant input variables were selected to construct the LSTM model. Then, the selected meteorological variables were refined by the relative importance of input variables to reconstruct the LSTM model. Finally, the LSTM model with final selected input variables is used to predict the precipitation and the performance is compared with other classical statistical algorithms and the machine learning algorithms. The experimental results show that the LSTM is suitable for precipitation prediction. The RNN models, combined with meteorological variables, could predict the precipitation accurately in Jingdezhen City and provide sufficient time to prepare strategies against potential related disasters.

Understanding and improving public flood risk perception is conducive to the implementation of effective flood risk management and disaster reduction policies. In the flood-prone city of Jingdezhen, flood disaster is one of the most destructive natural hazards to impact the society and economy. However, few studies have been attempted to focus on public flood risk perception in the small and medium-size city in China, like Jingdezhen. Therefore, the purpose of this study was to investigate the public flood risk perception in four districts of Jingdezhen and examine the related influencing factors. A questionnaire survey of 719 randomly sampled respondents was conducted in 16 subdistricts of Jingdezhen. Analysis of variance was conducted to identify the correlations between the impact factors and public flood risk perception. Then, the flood risk perception differences between different groups under the same impact factor were compared. The results indicated that the socio-demographic characteristics of the respondents (except occupation), flood experience, flood knowledge education, flood protection responsibility, and trust in government were strongly correlated with flood risk perception. The findings will help decision makers to develop effective flood risk communication strategies and flood risk reduction policies.

Timely and secure evacuation of residents during flood disasters or other emergency events is an important issue in urban community flood risk management, especially in vulnerable communities. An agent-based modeling framework was proposed in order to indicate how the community properties (e.g., community density and percentage of vulnerable residents), residents’ psychological attributes (e.g., flood risk tolerance threshold) and mutual aid mechanism affect the flood evacuation process. Results indicated that: (1) The community density negatively affected the flood evacuation efficiency. The greater the density of the community, the longer the evacuation time. (2) There was a negative correlation between the flood risk tolerance threshold of residents and evacuation efficiency. (3) The proportion of vulnerable resident agents had opposite effects on the evacuation efficiency of different types of communities, which was to negatively affect low-density communities and positively affect high-density communities. (4) Mutual aid mechanism can reduce evacuation time in low-density communities, and the effect was more pronounced with a higher proportion of vulnerable resident agents in the community. These findings can help managers to develop better emergency evacuation management for urban communities.

Quickly obtaining accurate waterlogging depth data is vital in urban flood events, especially for emergency response and risk mitigation. In this study, a novel approach to measure urban waterlogging depth was developed using images from social networks and traffic surveillance video systems. The Mask region-based convolutional neural network (Mask R-CNN) model was used to detect tires in waterlogging, which were considered to be reference objects. Then, waterlogging depth was calculated using the height differences method and Pythagorean theorem. The results show that tires detected from images can been used as an effective reference object to calculate waterlogging depth. The Pythagorean theorem method performs better on images from social networks, and the height differences method performs well both on the images from social networks and on traffic surveillance video systems. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

The initial concept of flood control has gradually shifted to flood risk management which emphasizes more public participation. Therefore, understanding the public’s protective coping behavioral patterns to floods is significant, and can help improve the effectiveness of public participation and implementation of flood-mitigation measures. However, the quantitative effect of socio-demographic factors on flood risk perception and behaviors is not clear. In this study, the socio-demographic factors are included to explore the quantitative relationship with and the affect path to flood protective coping behaviors with socio-demographic factors are studied. Shenzhen City in China is chosen as the study area, which suffers frequent urban floods every year. Questionnaire surveys are conducted in five flood-prone communities there, and 339 valid questionnaires were collected. The correlations between flood risk perception, flood risk knowledge, flood risk attitude, socio-demographic factors, and protective coping behaviors are analyzed firstly. A structural equation model (SEM) about these factors is then established to verify the correctness of hypothetical paths and discover new paths. The results indicates that socio-demographic factors and flood risk perception do not have impacts on protective coping behaviors directly, but are mediated by flood risk knowledge and flood risk attitude. Flood risk attitude is an important factor that affects protective coping behaviors directly. Moreover, two affect paths to flood protective coping behaviors are proposed. The findings of Shenzhen city in this study can be extended to other cities with similar characteristics, providing support for conducting effective flood mitigation measures.

Affected by global climate change and water shortages, food security continues to be challenged. Improving agricultural water use efficiency is essential to guarantee food security. China has been suffering from water scarcity for a long time, and insufficient water supply in the agricultural sector has seriously threatened regional food security and sustainable development. This study adopted the super-efficiency slack-based model (SBM) to measure the provincial agricultural water use efficiency (AWUE). Then, we applied the vector autoregression (VAR) Granger causality test and social network analysis (SNA) method to explore the spatial correlation of AWUE between different provinces and reveal the interprovincial transmission mechanism of spillover effects in AWUE. The results show the following: (1) In China, the provincial AWUE was significantly enhanced, and the gaps in provincial AWUE have widened in the past 20 years. (2) There were apparent spatial heterogeneity and correlations of provincial AWUE. The provinces with higher AWUE were mainly located in economically developed and coastal areas. (3) The correlation of AWUE between provinces showed significant network structure characteristics. Fujian, Hebei, Jiangsu, Shandong, and Hubei Qinghai were central to the network, with high centrality. (4) The AWUE spatial correlation network could be divided into four blocks. Each block played a different role in the cross-provincial transmission of spillover effects. Therefore, it is necessary to manage the agricultural water resources and improve water use efficiency from the perspective of the network.