Modeling, mapping and analysis of urban floods in India—a review on geospatial methodologies

Sundaram, Sreechanth; Devaraj, Suresh; Yarrakula, Kiran

doi:10.1007/s11356-021-16747-5

Cited by 24 publications

(6 citation statements)

References 130 publications

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“…The population density reflects the concentration of the population. The more concentrated the population, the more urban areas need to take disaster prevention and mitigation measures in advance and make emergency plans [54]. Medical service ability reflects whether the casualties can be quickly and effectively rescued after the disaster and whether the number of hospital beds and medical and health staff can meet the basic needs after the emergency.…”

Section: Establishing Evaluation Index Systemmentioning

confidence: 99%

“…Medical service ability reflects whether the casualties can be quickly and effectively rescued after the disaster and whether the number of hospital beds and medical and health staff can meet the basic needs after the emergency. Emergency rescue capability reflects the speed and quality of urban system rescue and disaster relief [54,55].…”

Section: Establishing Evaluation Index Systemmentioning

confidence: 99%

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Evaluation of Factors Found to Influence Urban Flood Resilience in China

Proverbs

2023

Water

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As one of the most frequently occurring natural hazards, flooding can seriously threaten global security and the sustainable development of our communities. Therefore, enhancing the resilience of cities and improving their ability to adapt to flooding have become issues of great significance. This study developed a new comprehensive evaluation model of flood resilience that includes an evaluation index system from the basis of four key dimensions of social resilience, economic resilience, ecological environment resilience and infrastructure resilience. Firstly, interpretative structural modelling (ISM) was applied to analyze the structural issues affecting urban flood resilience. Secondly, the analytic network process (ANP) was then used to calculate the importance of these indicators. Finally, taking three cities (Zhengzhou, Xi’an, and Jinan) in the Yellow River Basin of China as examples, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was used to evaluate their current levels of flood resilience using the findings from the earlier stages. The results show that the levels of rainfall and vulnerability of groups were the fundamental factors affecting urban flood resilience. Indicators such as average annual rainfall, fixed-asset investments, and emergency rescue capabilities were also found to have a greater impact on urban flood resilience. In the study area, Xi’an was found to have a higher level of resilience due to having strong ecological environmental resilience. These findings are expected to provide a useful reference for policymakers and stakeholders involved in the management of flooding events.

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Section: Establishing Evaluation Index Systemmentioning

confidence: 99%

Section: Establishing Evaluation Index Systemmentioning

confidence: 99%

Evaluation of Factors Found to Influence Urban Flood Resilience in China

Proverbs

2023

Water

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“…Waterlogging events on a global scale may have a serious and direct impact on the economy and humanitarianism, as well as continue to adversely affect economic development (Arshad et al, 2019). Globally, the occurrence of urban floods has been unprecedented resulting in huge economic and social losses (Sundaram et al, 2021). In July 2021, the rainstorm in Zhengzhou, China caused 380 deaths and a direct economic loss of 120.06 billion yuan.…”

Section: Open Access Edited Bymentioning

confidence: 99%

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

Zhang

Jian

Chen

et al. 2023

Front. Environ. Sci.

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In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations.

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“…In the past few decades, researchers have proposed different flood prediction models that utilize IoT and Artificial Intelligence (AI) technologies. These models encompass a range of approaches, including simple correlations based on stage-discharge data, deterministic models, Artificial Neural Networks (ANN), and fuzzy logic techniques [13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Thankappan,

Mary,

Yoon

et al. 2023

CMC

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Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in IoT networks. The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency. Real-world flood data is used for validation, demonstrating the superior performance of the AM-BP algorithm compared to traditional methods. In addition, multilayer high-end computing architecture (MLCA) is used to handle weather data such as rainfall, river water level, soil moisture, etc. The AM-BP's real-time abilities enable proactive flood management, facilitating timely responses and effective disaster mitigation. Furthermore, the AM-BP algorithm can analyze large and complex datasets, integrating environmental and climatic factors for more accurate flood prediction. The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%, 96.4% F1-Measure, 97% Precision, and 95.9% Recall. The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks, contributing to more resilient and efficient flood control strategies, and ensuring the safety and well-being of communities at risk of flooding.

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Modeling, mapping and analysis of urban floods in India—a review on geospatial methodologies

Cited by 24 publications

References 130 publications

Evaluation of Factors Found to Influence Urban Flood Resilience in China

Evaluation of Factors Found to Influence Urban Flood Resilience in China

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

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