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In recent years, there has been severe flooding in urban areas as well as coastal and river flooding. Urban flooding is exacerbated by climate change, urbanization, growing population, and the increase of impervious surfaces in urban areas. Stormwater drainage systems that discharge stormwater to a safe location in urban areas are becoming increasi...
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... the help of InfoWorks ICM program, flood analysis was performed and flood inundation maps were created. Stormwater drainage systems in the study area were numbered for easy and convenient interpretation of flood inundation maps ( Figure 6). In evaluating the flood performance of stormwater drainage systems, the analysis was conducted for different return periods and rainfall durations to examine the flood performance of the system under different rainfall intensities. ...
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Under the increasing stormwater events and consequent flood hazards, integrated flood management is increasingly becoming the most essential mitigation strategy to maximize the effectiveness of runoff reduction at the limitation of public budget. The research is aimed at proposed a multi-criteria combination approach to determine spatial interventi...
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... The 2D surface inundation model uses the 2D finite volume method to solve shallow flow equations. The Rienmann solver and TVD (Total Variation Diminishing) excitation techniques are used to solve the model [49][50][51]. The 2D surface inundation model is used to simulate the stagnant water flow generated by the drainage network system when passing through the complex geometric terrain. ...
With the increase in global extreme climate events, the frequency of urban waterlogging caused by extreme rainstorms is increasing, resulting in serious economic losses and risk to local residents. Understanding the influence of impervious surfaces on urban waterlogging is of great significance for reducing urban waterlogging disasters. Based on InfoWorks ICM, the urban waterlogging model of Lin’an City was established, and the multi-scenario design method was used to analyze the characteristics and causes of urban waterlogging under different designed rainfall return periods. The results show that the maximum stagnant water depth and area are positively correlated with the proportion of impervious surfaces and rainfall return periods. In addition, urban waterlogging is related to the fragmentation of impervious surfaces, pipeline network, and so on. Based on the findings, it is suggested that impervious surfaces should be placed upstream and along roads where feasible. It is also recommended that the aggregation of impervious surfaces is minimized to prevent urban waterlogging. The results provide technical support and reference for local governments to prevent waterlogging disasters.
Water scarcity is a pressing issue, intensified by factors such as population growth and industrialization. Hence, it is crucial to monitor, conserve and analyse groundwater resources, which are essential sources of clean and usable water. This study examines changes in groundwater levels (GWLs) in northeastern Turkey's mountainous and snow-covered area. The primary objective is to assess the effectiveness of integrated machine learning models, specifically, the extreme learning machine (ELM) technique combined with signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and empirical wavelet transform (EWT) for monthly GWL prediction models. Seventy percent of the accessible data is allocated for training, while 30% is designated for testing. A correlation matrix involving precipitation, temperature, relative humidity and GWL parameters is generated with inputs that possess significant correlations being selected, such as GWLt−1, GWLt−2, RHt, RHt−1 and RHt−2. To evaluate model results, various metrics, including mean squared error, mean absolute error, mean absolute percentage error, mean bias error, bias factor, determination coefficient, Nash-Sutcliffe efficiency, as well as tools such as box plots, Taylor diagrams and radar charts, are utilized to compare outcomes during the interpretation phase. The results of the analyses show that applying data decomposition methods such as EEMD, VMD and EWT significantly improves the performance of the ELM algorithm in predicting GWLs. VMD-ELM is the most accurate for GWL forecasting among the approaches examined. The R2 values of the most successful models established in the two wells are 0.993 and 0.905. The outcomes of this research hold significance for decision-makers and policymakers as it offers informative insights into aquifer surveillance, irrigation strategizing and efficient administration of water resources.
