AIMC Topic: Floods

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Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Environment international
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...

Improving flood-prone areas mapping using geospatial artificial intelligence (GeoAI): A non-parametric algorithm enhanced by math-based metaheuristic algorithms.

Journal of environmental management
Flooding presents substantial dangers to human lives and infrastructure, underscoring the need to map flood-prone areas to implement effective mitigation measures precisely. Although machine learning algorithms have made great strides, their accuracy...

Assessment of urban flood susceptibility based on a novel integrated machine learning method.

Environmental monitoring and assessment
Flood susceptibility assessment is the premise and foundation to prevent flood disaster events effectively. To accurately assess urban flood susceptibility (UFS), this study first analyzes the advantages and disadvantages of multi-layer perceptron (M...

Incorporating dynamic drainage supervision into deep learning for accurate real-time flood simulation in urban areas.

Water research
Urban flooding has become a prevalent issue in cities worldwide. Urban flood dynamics differ significantly from those in natural watersheds, primarily because of the intricate drainage systems and the high spatial heterogeneity of urban surfaces, whi...

Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan.

Journal of environmental management
Due to its diverse topography, Pakistan faces different types of floods each year, which cause substantial physical, environmental, and socioeconomic damage. However, the susceptibility of specific regions to different flood types remains unexplored....

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system.

Water research
Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that us...

Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.

Environmental science and pollution research international
This study was carried out with the aim of applying Condorcet and Borda scoring algorithms based on Game Theory (GT) to determine flood points and Flood Susceptibility Mapping (FSM) based on Machine Learning Algorithms (MLA) including Random Forest (...

Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Environmental science and pollution research international
Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (M...

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment.

Environmental science and pollution research international
Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptati...

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.

Journal of environmental management
This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 a...