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Detection of urban flood inundation from traffic images using deep learning methods
Urban hydrological monitoring is essential for analyzing urban hydrology and controlling
storm floods. However, runoff monitoring in urban areas, including flood inundation depth, is …
storm floods. However, runoff monitoring in urban areas, including flood inundation depth, is …
Enhancing Flood susceptibility modeling: a hybrid deep neural network with statistical learning algorithms for Predicting Flood Prone Areas
M Ghobadi, M Ahmadipari - Water Resources Management, 2024 - Springer
Flooding, with its environmental impact, represents a naturally destructive process that
typically results in severe damage. Consequently, accurately identifying flood-prone areas …
typically results in severe damage. Consequently, accurately identifying flood-prone areas …
Hybrid iterative and tree-based machine learning algorithms for lake water level forecasting
Accurate forecasting of lake water level (WL) fluctuations is essential for effective
development and management of water resource systems. This study applies the Random …
development and management of water resource systems. This study applies the Random …
Floodplain lake water level prediction with strong river-lake interaction using the ensemble learning LightGBM
Timely and accurate prediction of water levels is crucial for managing floodplain lakes with
important ecosystem services, especially for flood prevention. Floodplain lakes are …
important ecosystem services, especially for flood prevention. Floodplain lakes are …
Study on runoff simulation with multi-source precipitation information fusion based on multi-model ensemble
R Li, C Liu, Y Tang, C Niu, Y Fan, Q Luo… - Water Resources …, 2024 - Springer
High-quality precipitation data input and the selection of reasonable and applicable
hydrological models are the main ways to improve the accuracy of runoff simulation, and are …
hydrological models are the main ways to improve the accuracy of runoff simulation, and are …
Develo** extended and unscented kalman filter-based neural networks to predict cluster-induced roughness in gravel bed rivers
Flow resistance in natural gravel-bed rivers must be precisely predicted in order for water-
related infrastructure to be designed effectively. Cluster microforms are significant factors in …
related infrastructure to be designed effectively. Cluster microforms are significant factors in …
Comparison of classical and machine learning methods in estimation of missing streamflow data
AB Dariane, MI Borhan - Water Resources Management, 2024 - Springer
Recovering missing data and access to a complete and accurate streamflow data is of great
importance in water resources management. This article aims to comparatively investigate …
importance in water resources management. This article aims to comparatively investigate …
Investigating the impact of cumulative pressure-induced stress on machine learning models for pipe breaks
Significant financial resources are needed for the maintenance and rehabilitation of water
supply networks (WSNs) to prevent pipe breaks. The causes and mechanisms for pipe …
supply networks (WSNs) to prevent pipe breaks. The causes and mechanisms for pipe …
Novel hybrid machine learning algorithms for lakes evaporation and power production using floating semitransparent polymer solar cells
The present study predicts the future evaporation losses by applying novel hybrid Machine
Learning Algorithms (MLA). Water resources management is achieved by covering the …
Learning Algorithms (MLA). Water resources management is achieved by covering the …
Improving hybrid models for precipitation forecasting by combining nonlinear machine learning methods
Precipitation forecast is key for water resources management in semi-arid climates. The
traditional hybrid models simulate linear and nonlinear components of precipitation series …
traditional hybrid models simulate linear and nonlinear components of precipitation series …