Riverine Flood Prediction and Early Warning in Mountainous Regions using Artificial Intelligence

• URL: http://arxiv.org/abs/2505.18645v1
• Date: Sat, 24 May 2025 11:10:09 GMT
• Title: Riverine Flood Prediction and Early Warning in Mountainous Regions using Artificial Intelligence
• Authors: Haleema Bibi, Sadia Saleem, Zakia Jalil, Muhammad Nasir, Tahani Alsubait,
• Abstract summary: This study uses the Kabul River between Pakistan and Afghanistan as a case study to reflect the complications of flood forecasting in transboundary basins.<n> utilizing satellite-based climatic data, this study applied numerous advanced machine-learning and deep learning models to predict daily and multi-step river flow.<n>The LSTM network outperformed other models, achieving the highest R2 value of 0.96 and the lowest RMSE value of 140.96 m3/sec.
• Score: 0.9320657506524149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flooding is the most devastating phenomenon occurring globally, particularly in mountainous regions, risk dramatically increases due to complex terrains and extreme climate changes. These situations are damaging livelihoods, agriculture, infrastructure, and human lives. This study uses the Kabul River between Pakistan and Afghanistan as a case study to reflect the complications of flood forecasting in transboundary basins. The challenges in obtaining upstream data impede the efficacy of flood control measures and early warning systems, a common global problem in similar basins. Utilizing satellite-based climatic data, this study applied numerous advanced machine-learning and deep learning models, such as Support Vector Machines (SVM), XGBoost, and Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU) to predict daily and multi-step river flow. The LSTM network outperformed other models, achieving the highest R2 value of 0.96 and the lowest RMSE value of 140.96 m3/sec. The time series LSTM and GRU network models, utilized for short-term forecasts of up to five days, performed significantly. However, the accuracy declined beyond the fourth day, highlighting the need for longer-term historical datasets for reliable long-term flood predictions. The results of the study are directly aligned with Sustainable Development Goals 6, 11, 13, and 15, facilitating disaster and water management, timely evacuations, improved preparedness, and effective early warning.

Related papers

• RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [11.045126693185377]
  We present RiverMamba, a novel deep learning model that is pretrained with long-term reanalysis data.<n>To achieve this, RiverMamba leverages efficient Mamba blocks that enable the model to capture global-scale channel routing.<n>Our analysis demonstrates that RiverMamba delivers reliable predictions of river discharge, including extreme floods across return periods and lead times.
  arXiv  Detail & Related papers  (2025-05-28T16:21:58Z)
• A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting [0.9487148673655145]
  In July 2017, the cities of Goslar and G"ottingen experienced severe flood events characterized by short warning time of only 20 minutes.<n>This highlights the critical need for a more reliable and timely flood forecasting system.
  arXiv  Detail & Related papers  (2025-03-25T10:14:54Z)
• Global spatio-temporal downscaling of ERA5 precipitation through generative AI [3.320484236699228]
  We introduce SpateGAN-ERA5, the first deep learning based-temporal downscaling of precipitation data on a global scale. SpateGAN-ERA5 uses a conditional generative adversarial neural network (cGAN) that enhances the spate of ERA5 precipitation data 24 km and 1 hour to 2 km and 10 minutes. It delivers high-resolution rainfall fields with realistic, large-temporal patterns and accurate rain rate distribution including extremes.
  arXiv  Detail & Related papers  (2024-11-22T14:11:23Z)
• GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction [0.10923877073891446]
  Seawater intrusion into coastal aquifers poses a significant threat to groundwater resources. We develop GeoFUSE, a novel deep-learning-based surrogate framework. We apply GeoFUSE to a 2D cross-section of the Beaver Creek tidal stream-floodplain system in Washington State.
  arXiv  Detail & Related papers  (2024-10-26T08:10:32Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• AI Increases Global Access to Reliable Flood Forecasts [6.498815455353096]
  Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
  arXiv  Detail & Related papers  (2023-07-30T01:49:21Z)
• Rapid Flood Inundation Forecast Using Fourier Neural Operator [77.30160833875513]
  Flood inundation forecast provides critical information for emergency planning before and during flood events. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
  arXiv  Detail & Related papers  (2023-07-29T22:49:50Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.