ABSTRACT
With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.
Subject(s)
Floods , Environmental Monitoring/methods , Cities , Models, Theoretical , Climate ChangeABSTRACT
Organic materials with rich active sites are good candidates of high-capacity anodes in aqueous batteries, but commonly low utilization of active sites limits their capacity. Herein, two isomers, symmetric and asymmetric hexaazatribenzanthraquinone (s-HATBAQ and a-HATBAQ), with rich active sites have been synthesized in a controllable manner. It has been revealed for the first time that a sulfuric acid catalyst can facilitate the stereoselective formation of s-HATBAQ. Attributed to the reduced steric hindrance in favor of proton insertion as well as the amorphous structure conducive to electrochemical dynamics, s-HATBAQ exhibits 1.5 times larger specific capacity than a-HATBAQ. Consequently, the electrode of s-HATBAQ with 50% reduced graphene oxide (s-HATBAQ-50%rGO) delivers a record high specific capacity of 405 mAh g-1 in H2SO4 electrolyte. Moreover, the assembled MnO2//s-HATBAQ-50%rGO aqueous proton full batteries show an exceptional cycling stability at 25°C and can maintain â¼92% capacity after 1000 cycles at 0.5 A g-1 at -80°C. This work demonstrates the controllable synthesis of isomers, showcases a wide-temperature-range prototype proton battery and highlights the significance of precise molecular structure modulation in organic energy storage.