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USA-MD-MONROVIA Azienda Directories
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Azienda News:
- Flood Prediction Using Machine Learning Models: Literature Review - MDPI
For instance, the prediction of streamflow in a long-term flood prediction scenario depends on soil moisture estimates in a catchment, in addition to rainfall [62]
- A Review of Hydrodynamic and Machine Learning Approaches for Flood . . .
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world In this paper, we reviewed recently published literature on ML and DL
- A Comprehensive Review of Methods for Hydrological Forecasting Based on . . .
For example, in the context of flood predictions within crucial river basins, which encompasses numerous factors such as personnel safety, property transfer, disease transmission, and road network damage, enhancing the interpretability and credibility of hydrological forecasting can facilitate more informed decision making among managers
- The State of the Art in Deep Learning Applications, Challenges, and . . .
DNNs can forecast probable flood levels based on previous data and meteorological factors like rainfall and river discharge The model’s several hidden layers enable feature extraction from the data and learning from it, increasing prediction precision
- Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi . . .
Flood prediction requires continuous understanding and processing of hydrological data, such as rainfall and river water levels, which form the contexts for future flood predictions
- Flood Forecasting by Using Machine Learning: A Study Leveraging . . .
The potential of using machine learning algorithms for flood rainfall prediction and the importance of the problem cannot be denied The severity and frequency of flood occurrences are expected to rise due to the dual threats of a fast-warming environment and increasing urbanisation, endangering more people’s lives, ecological systems, and
- Deep Learning Ensemble for Flood Probability Analysis - MDPI
Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, and challenges in spatial and temporal scaling This research innovatively uses only three remotely sensed and computed factors: rainfall, runoff and temperature We also employ three deep learning models—Feedforward Neural Network (FNN
- Research on Water Resource Modeling Based on Machine Learning . . . - MDPI
This review examines the application of machine learning in key hydrological factors, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality
- Comprehensive Overview of Flood Modeling Approaches: A Review of Recent . . .
The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis Additionally, it covers the heuristic and metaheuristic techniques employed in flood control
- Leveraging Recurrent Neural Networks for Flood Prediction and . . . - MDPI
Future research should focus on enhancing the accuracy and robustness of machine learning models for flood prediction by integrating static catchment attributes, such as soil properties, land use, and topography, to provide additional contextual information
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