AIMC Topic: Floods

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

Sensors (Basel, Switzerland) Oct 27, 2022
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...
Floods Image Processing, Computer-Assisted Artificial Intelligence Water Algorithms Neural Networks, Computer
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Modelling flood susceptibility based on deep learning coupling with ensemble learning models.

Journal of environmental management Oct 11, 2022
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...
Deep Learning Disasters Forests Floods ROC Curve
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Improved runoff forecasting based on time-varying model averaging method and deep learning.

PloS one Sep 15, 2022
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...
Floods Water Movements Deep Learning Forecasting Rain
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Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.

Water research Sep 14, 2022
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...
Computer Simulation Water Neural Networks, Computer Floods Hydrodynamics
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Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.

Journal of environmental management May 19, 2022
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...
Geographic Information Systems Deep Learning Algorithms Floods Neural Networks, Computer
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Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning.

Computational intelligence and neuroscience May 10, 2022
With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatchi...
Problem Solving Floods Deep Learning Water Algorithms
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Deep learning models to predict flood events in fast-flowing watersheds.

The Science of the total environment Nov 23, 2021
This study aims to explore the reliability of flood warning forecasts based on deep learning models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify the applicability of flood event predictions for a river with flood ...
Reproducibility of Results Cities Deep Learning Floods Rivers
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Artificial Intelligence in Geospatial Analysis for Flood Vulnerability Assessment: A Case of Dire Dawa Watershed, Awash Basin, Ethiopia.

TheScientificWorldJournal Nov 22, 2021
This study presents the novelty artificial intelligence in geospatial analysis for flood vulnerability assessment in Dire Dawa, Ethiopia. Flood-causing factors such as rainfall, slope, LULC, elevation NDVI, TWI, SAVI, K-factor, R-factor, river distan...
Environmental Monitoring Floods Rivers Geographic Information Systems Ethiopia Artificial Intelligence Cities
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Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method.

Sensors (Basel, Switzerland) Aug 20, 2021
Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, ...
Humans Neural Networks, Computer Floods Image Processing, Computer-Assisted
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Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future.

Journal of environmental management Aug 17, 2021
The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using sev...
Climate Floods ROC Curve Machine Learning Forecasting
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