Fine Flood Forecasts: Incorporating local data into global models through fine-tuning

• URL: http://arxiv.org/abs/2504.12559v1
• Date: Thu, 17 Apr 2025 01:14:21 GMT
• Title: Fine Flood Forecasts: Incorporating local data into global models through fine-tuning
• Authors: Emil Ryd, Grey Nearing,
• Abstract summary: Floods are the most common form of natural disaster and accurate flood forecasting is essential for early warning systems.<n>Previous work has shown that machine learning (ML) models are a promising way to improve flood predictions when trained on large, geographically-diverse datasets.<n>This requirement of global training can result in a loss of ownership for national forecasters who cannot easily adapt the models to improve performance in their region.<n>We provide a roadmap for national forecasters who wish to take ownership of global models using their own data, aiming to lower the barrier to operational deployment of ML-based hydrological forecast systems.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Floods are the most common form of natural disaster and accurate flood forecasting is essential for early warning systems. Previous work has shown that machine learning (ML) models are a promising way to improve flood predictions when trained on large, geographically-diverse datasets. This requirement of global training can result in a loss of ownership for national forecasters who cannot easily adapt the models to improve performance in their region, preventing ML models from being operationally deployed. Furthermore, traditional hydrology research with physics-based models suggests that local data -- which in many cases is only accessible to local agencies -- is valuable for improving model performance. To address these concerns, we demonstrate a methodology of pre-training a model on a large, global dataset and then fine-tuning that model on data from individual basins. This results in performance increases, validating our hypothesis that there is extra information to be captured in local data. In particular, we show that performance increases are most significant in watersheds that underperform during global training. We provide a roadmap for national forecasters who wish to take ownership of global models using their own data, aiming to lower the barrier to operational deployment of ML-based hydrological forecast systems.

Related papers

• Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience [1.0499611180329806]
  Climate change increases the frequency of extreme rainfall, placing a significant strain on urban infrastructures, especially Combined Sewer Systems (CSS) Overflows from overburdened CSS release untreated wastewater into surface waters, posing environmental and public health risks. Traditional physics-based models are effective, but they are costly to maintain and difficult to adapt to evolving system dynamics. Machine Learning approaches offer cost-efficient alternatives with greater adaptability.
  arXiv  Detail & Related papers  (2025-04-24T11:52:13Z)
• Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models [1.8067095934521364]
  Recent machine learning (ML) models can harness vast datasets for accurate predictions at large spatial scales. This study explores questions regarding model design and data needed for inputs and training to improve performance.
  arXiv  Detail & Related papers  (2024-10-23T15:36:59Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• 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)
• 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)
• Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data [9.792805355704203]
  We study the loss landscape of model averaging in federated learning (FL) We decompose the expected loss of the global model into five factors related to the client models. We propose utilizing IMA on the global model at the late training phase to reduce its deviation from the expected speed.
  arXiv  Detail & Related papers  (2023-05-13T06:19:55Z)
• 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)
• Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions [59.284907093349425]
  Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. We provide a language for describing how training data influences predictions, through a causal framework. Our framework bypasses the need to retrain expensive models and allows us to estimate causal effects based on observational data alone.
  arXiv  Detail & Related papers  (2022-07-28T17:36:24Z)
• Increasing the accuracy and resolution of precipitation forecasts using deep generative models [3.8073142980733]
  We train a conditional Generative Adversarial Network -- coined CorrectorGAN -- to produce ensembles of high-resolution, bias-corrected forecasts. CorrectorGAN, once trained, produces predictions in seconds on a single machine. Results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions.
  arXiv  Detail & Related papers  (2022-03-23T09:45:12Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• Flood forecasting with machine learning models in an operational framework [0.0]
  The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public. The forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287,000 km2, home to more than 350M people.
  arXiv  Detail & Related papers  (2021-11-04T11:58:31Z)
• MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images [4.7791671364702575]
  Land-use regression models are important for the assessment of air pollution concentrations in areas without measurement stations. We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data.
  arXiv  Detail & Related papers  (2020-02-18T11:21:55Z)

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.