AIMC Topic: Floods

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A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models.

Environmental science and pollution research international Apr 29, 2024
The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (A...
Algorithms Models, Theoretical Machine Learning Spatial Analysis Floods Support Vector Machine
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Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping.

Environmental science and pollution research international Apr 26, 2024
Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of thes...
Rivers Floods Machine Learning Algorithms Support Vector Machine Iran
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Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Environmental science and pollution research international Apr 3, 2024
Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonst...
Humans Neural Networks, Computer Machine Learning Bangladesh Support Vector Machine Floods
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Global prediction of extreme floods in ungauged watersheds.

Nature Mar 20, 2024
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula...
Disaster Planning Floods Calibration Hydrology Computer Simulation Reproducibility of Results Time Factors Rivers Artificial Intelligence Forecasting
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Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques.

Environmental research Feb 26, 2024
Real-time flood forecasting is one of the most pivotal measures for flood management, and real-time error correction is a critical step to guarantee the reliability of forecasting results. However, it is still challenging to develop a robust error co...
Floods Machine Learning Forecasting Computer Simulation Reproducibility of Results
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Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches.

Environmental research Dec 29, 2023
Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activiti...
Risk Assessment Iran Floods Machine Learning Risk Factors Humans
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Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China.

Journal of environmental management Dec 5, 2023
Deep learning techniques have offered innovative and efficient tools for accurate and automated detection of sewer defects by leveraging large-scale sewer data and advanced feature learning algorithms. However, there has been a lack of thorough chara...
Algorithms Deep Learning Hydrodynamics China Floods
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Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk.

Journal of environmental management Aug 16, 2023
Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning - an updated version of machine learning techniques - and multi-criteria...
Floods Neural Networks, Computer Decision Making Risk Assessment Deep Learning
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Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall-snowmelt regime Munzur River, Türkiye.

Environmental science and pollution research international Aug 12, 2023
Revealing the dynamic link between rainfall and runoff, which are the main components of the hydrological cycle, is significant for the planning and managing water resources, disaster risk management, and construction of water structures. This study ...
Neural Networks, Computer Rivers Floods Algorithms Fuzzy Logic
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Assessment of coastal vulnerability using integrated fuzzy analytical hierarchy process and geospatial technology for effective coastal management.

Environmental science and pollution research international Jun 30, 2023
The vulnerability of coastal regions to climate change is a growing global concern, particularly in Bangladesh, which is vulnerable to flooding and storm surges due to its low-lying coastal areas. In this study, we used the fuzzy analytical hierarchy...
Floods Climate Change Fuzzy Logic Bangladesh
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