AIMC Topic: Floods

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Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye.

Environmental science and pollution research international
With the effect of global warming, the frequency of floods, one of the most important natural disasters, increases, and this increases the damage it causes to people and the environment. Flood routing models play an important role in predicting flood...

Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model.

International journal of environmental research and public health
At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow...

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention.

Sensors (Basel, Switzerland)
River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient ...

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction.

Journal of environmental management
Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flo...

Unraveling the complexities of urban fluvial flood hydraulics through AI.

Scientific reports
As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; ho...

FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring.

Sensors (Basel, Switzerland)
In satellite remote sensing applications, waterbody segmentation plays an essential role in mapping and monitoring the dynamics of surface water. Satellite image segmentation-examining a relevant sensor data spectrum and identifying the regions of in...

Modelling flood susceptibility based on deep learning coupling with ensemble learning models.

Journal of environmental management
Modelling flood susceptibility is an indirect way to reduce the loss from flood disaster. Now, flood susceptibility modelling based on data driven model is state-of-the-art method such as ensemble learning and deep learning. However, the effect of de...

Improved runoff forecasting based on time-varying model averaging method and deep learning.

PloS one
In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constru...

Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.

Water research
The computational limitations of complex numerical models have led to adoption of statistical emulators across a variety of problems in science and engineering disciplines to circumvent the high computational costs associated with numerical simulatio...

Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.

Journal of environmental management
It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respe...