Attention-based Models for Snow-Water Equivalent Prediction

• URL: http://arxiv.org/abs/2311.03388v1
• Date: Fri, 3 Nov 2023 23:33:35 GMT
• Title: Attention-based Models for Snow-Water Equivalent Prediction
• Authors: Krishu K. Thapa, Bhupinderjeet Singh, Supriya Savalkar, Alan Fern, Kirti Rajagopalan, Ananth Kalyanaraman
• Abstract summary: Snow Water-Equivalent (SWE) -- the amount of water available if snowpack is melted -- is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. We present a generic attention-based modeling framework for SWE prediction and adapt it to spatial attention and temporal attention. Our results on 323 SNOTEL stations in the U.S. demonstrate that our attention-based models outperform other machine learning approaches.
• Score: 12.340236862664195
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Snow Water-Equivalent (SWE) -- the amount of water available if snowpack is melted -- is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. SWE values vary spatiotemporally -- affected by weather, topography and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporally complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.

Related papers

• 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)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting [1.642094639107215]
  We introduce Graph Dual-streamtemporal Conal Attention Fusion (GD-CAF) to learn from historical graph of precipitation maps and nowcast future time step ahead. GD-CAF consists of gated-temporal convolutional attention as well as fusion modules equipped with depthwise-separable convolutional operations. We evaluate our model seven years of precipitation maps across Europe and its neighboring areas collected from the ERA5 dataset.
  arXiv  Detail & Related papers  (2024-01-15T20:54:20Z)
• Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces [78.08947381962658]
  Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary dynamical systems. We learn the evolution of such non-stationary systems using a novel recurrent neural operator (RNO), which learns mappings between function spaces. We propose a conformal prediction framework to forecast tipping points by monitoring deviations from physics constraints.
  arXiv  Detail & Related papers  (2023-08-17T05:42:27Z)
• Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles [7.434517639563671]
  Knowing actual precipitation in space and time is critical in hydrological modelling applications. Gridded satellite precipitation datasets offer an alternative option for estimating the actual precipitation. To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products.
  arXiv  Detail & Related papers  (2023-02-02T20:03:21Z)
• 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)
• Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed [15.963061568077567]
  We show that our simple model can be trained much faster than complex attention networks such as GeoMAN. We also demonstrate that extreme events can be predicted in geographical locations separate from locations observed during the training process. This spatially-inductive setting enables us to predict extreme events in other areas in the US and other parts of the world using our model trained with the Wabash Basin data.
  arXiv  Detail & Related papers  (2021-04-25T02:26:09Z)
• Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems [3.6704226968275258]
  We propose a hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN) model. Our model consists of two stages: a spatial attention-based encoder and a temporal attention-based decoder. Experiments on a real-world dataset are conducted and demonstrate that our model outperformed 9 baseline models in flow and pressure series prediction.
  arXiv  Detail & Related papers  (2020-04-13T09:00:26Z)
• Time series and machine learning to forecast the water quality from satellite data [0.0]
  Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms. For monitoring, pollution control boards will need nowcasts and forecasts of any pollution.
  arXiv  Detail & Related papers  (2020-03-16T18:16:44Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

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.