Evaluation of deep learning models for Australian climate extremes: prediction of streamflow and floods

• URL: http://arxiv.org/abs/2407.15882v1
• Date: Sat, 20 Jul 2024 23:45:04 GMT
• Title: Evaluation of deep learning models for Australian climate extremes: prediction of streamflow and floods
• Authors: Siddharth Khedkar, R. Willem Vervoort, Rohitash Chandra,
• Abstract summary: In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting small to medium-sized climate extreme events over a short time horizon. We present an ensemble-based machine learning approach that addresses large-scale extreme flooding challenges.
• Score: 0.17999333451993949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia, causing damage to the environment and economy and losses of human and animal lives. An efficient method of forecasting floods is crucial to limit this damage. Techniques for flood prediction are currently based on hydrological, and hydrodynamic (physically-based) numerical models. Machine learning methods that include deep learning offer certain advantages over conventional physically based approaches, including flexibility and accuracy. Deep learning methods have been promising for predicting small to medium-sized climate extreme events over a short time horizon; however, large flooding events present a critical challenge. We present an ensemble-based machine learning approach that addresses large-scale extreme flooding challenges using a switching mechanism motivated by extreme-value theory for long-short-term-memory (LSTM) deep learning models. We use a multivariate and multi-step time-series prediction approach to predict streamflow for multiple days ahead in the major catchments of Australia. The ensemble framework also employs static information to enrich the time-series information, allowing for regional modelling across catchments. Our results demonstrate enhanced prediction of streamflow extremes, with notable efficacy for large flooding scenarios in the selected Australian catchments. Through comparative analysis, our methodology underscores the potential for deep learning models to revolutionise flood forecasting across diverse regions.

Related papers

• Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations [0.3413711585591077]
  We present a systematic framework for training high-fidelity Deep Vision-based coastal flood prediction models in low-data settings. We also introduce a deep CNN architecture tailored specifically to the coastal flood prediction problem at hand. The performance of the developed DL models is validated against commonly adopted geostatistical regression methods.
  arXiv  Detail & Related papers  (2024-06-06T19:54:34Z)
• Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
  A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan. textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• 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)
• 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)
• Fully Convolutional Networks for Dense Water Flow Intensity Prediction in Swedish Catchment Areas [7.324969824727792]
  We propose a machine learning-based approach for predicting water flow intensities in inland watercourses. We are the first to tackle the task of dense water flow intensity prediction.
  arXiv  Detail & Related papers  (2023-04-04T09:28:36Z)
• 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)
• Flood Prediction Using Machine Learning Models [0.0]
  This paper aims to reduce the extreme risks of this natural disaster by providing a prediction for floods using different machine learning models. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.
  arXiv  Detail & Related papers  (2022-08-02T03:59:43Z)
• 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)
• Deep Learning based Extreme Heatwave Forecast [8.975667614727648]
  Using 1000 years of state-of-the-art PlaSim Planete Simulator Climate Model data, it is shown that Convolutional Neural Network-based Deep Learning frameworks, with large-class undersampling and transfer learning achieve significant performance in forecasting the occurrence of extreme heatwaves.
  arXiv  Detail & Related papers  (2021-03-17T16:10:06Z)

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.