Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin

Lei

et al. 2023

River

Extreme climate and urbanization lead to frequent urban floods, and urban water system dispatching is an important means of urban flood control. A coupled model is proposed to simulate urban floods in real-time and achieve comprehensive water system control for urban floods. First, the systematical description of coordinated drainage and regulation model of urban water systems in China is introduced, then the practice of Fuzhou city was taken as an example to introduce the prediction and forecast systems, as well as water system regulation for urban flood and waterlogging disaster in detail. Results show that the root-mean-square error of river flood prediction is less than 0.3 and that the calculation time of waterlogging simulation is less than 10 min; the optimization regulation reduces the peak river water level by 31 cm. This study will establish a novel urban water system management model to help urban managers better reduce the loss caused by urban floods.

Section: A Coordinated Drainage and Regulation Modelmentioning

confidence: 99%

Section: Prediction and Forecast Systems For Urban Flood And Waterlog...mentioning

confidence: 99%

A coordinated drainage and regulation model of urban water systems in China: A case study in Fuzhou city

Lei

et al. 2023

River

“…Spatial data representing these factors were converted into a 30 m resolution raster format using ArcGIS and nearest neighbor interpolation. The natural break method was employed to classify the data into distinct categories, facilitating their seamless integration into the flood susceptibility modeling framework (Jiang et al, 2022). The topography factors included Slope (SL), Aspect (AS), Altitude (AL), Topographic Position Index (TPI), and Terrain Ruggedness Index (TRI).…”

Section: Preparing Flood Influencing Factorsmentioning

confidence: 99%

Quantitative Assessment of Future Land Use Changes' Impact on Flood Risk Components: Integration of Remote Sensing, Machine Learning, and Hydraulic Modeling

Gholami,

Li,

Zhang

et al. 2023

Preprint

Flood is one of the most devastating natural hazards that has intensified due to land use land cover (LULC) changes in recent years. Flood risk assessment is crucial task for disaster management activities in flood-prone areas. In this study, we proposed a flood risk assessment framework that combines flood vulnerability, hazard, and damages under long-term LULC changes in the Tajan watershed, northern Iran. The research analyzed historical land use change trends and predicted changes up to 2040 by employing GIS, remote sensing, and land change modeling. The flood vulnerability map was generated using the Random Forest model, incorporating historical data from 332 flooded locations and 12 geophysical and anthropogenic flood factors under LULC change scenarios. The potential flood damage costs in residential and agricultural areas, considering long-term LULC changes, were calculated using the HEC-RAS hydraulic model and a global damage function. Results revealed that unplanned urban growth, agricultural expansion, and deforestation near the river downstream amplify flood risk in 2040. High and very high flood vulnerability areas would increase by 43% in 2040 due to human activities and LULC changes. Estimated annual flood damage for agriculture and built-up areas was projected to surge from $162 million to $376 million and $91 million to $220 million, respectively, considering 2021 and 2040 land use change scenarios in the flood-prone region. The research highlights the importance of land use planning in mitigating flood-associated risks, both in the studied area and other flood-prone regions.

“…As a key aspect of water hazard control, accurate forecasting of river runoff is important. Under the influence of changing environment, the runoff formation process and evolution mechanism are complex and variable, and the basin steady-state assumption is no longer valid [1][2]. Currently, it is important to explore disaster forecasting models suitable for different basins to achieve proactive and scientific flood control [3].…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Liu

Li-hu

et al. 2023

IEEE Access

To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (Ens) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.