Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales

• URL: http://arxiv.org/abs/2510.09500v1
• Date: Fri, 10 Oct 2025 16:04:35 GMT
• Title: Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
• Authors: Shiyuan Luo, Runlong Yu, Shengyu Chen, Yingda Fan, Yiqun Xie, Yanhua Li, Xiaowei Jia,
• Abstract summary: GeoSTAR-S is a framework for predicting stream water temperature across different watersheds and spatial scales.<n>The major innovation of GeoSTAR-S is the introduction of geo-aware embedding.<n>We evaluate GeoSTAR-S's efficacy in predicting stream water temperature, which is a master factor for water quality.
• Score: 30.77342818734587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to varying spatial regions and scales due to the inherent data heterogeneity presented in real environmental ecosystems. This generalization issue is further exacerbated by the limited observation samples available for model training. To address these issues, we propose Geo-STARS, a geo-aware spatio-temporal modeling framework for predicting stream water temperature across different watersheds and spatial scales. The major innovation of Geo-STARS is the introduction of geo-aware embedding, which leverages geographic information to explicitly capture shared principles and patterns across spatial regions and scales. We further integrate the geo-aware embedding into a gated spatio-temporal graph neural network. This design enables the model to learn complex spatial and temporal patterns guided by geographic and hydrological context, even with sparse or no observational data. We evaluate Geo-STARS's efficacy in predicting stream water temperature, which is a master factor for water quality. Using real-world datasets spanning 37 years across multiple watersheds along the eastern coast of the United States, Geo-STARS demonstrates its superior generalization performance across both regions and scales, outperforming state-of-the-art baselines. These results highlight the promise of Geo-STARS for scalable, data-efficient environmental monitoring and decision-making.

Related papers

• GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
  This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
  arXiv  Detail & Related papers  (2026-02-13T04:48:05Z)
• GeoBS: Information-Theoretic Quantification of Geographic Bias in AI Models [34.611626290720295]
  We establish an information-theoretic framework for geo-bias evaluation, called GeoBS (Geo-Bias Scores)<n>We propose three novel geo-bias scores that explicitly take intricate spatial factors into consideration.
  arXiv  Detail & Related papers  (2025-09-27T20:07:21Z)
• DIGMAPPER: A Modular System for Automated Geologic Map Digitization [6.326773326196182]
  We present DIGMAPPER, a system developed in collaboration with the United States Geological Survey (USGS) to automate the digitization of geologic maps.<n> DIGMAPPER features a fully dockerized, workflow-orchestrated architecture that integrates state-of-the-art deep learning models for map layout analysis, feature extraction, and georeferencing.<n> Evaluations on over 100 annotated maps from the DARPA-USGS dataset demonstrate high accuracy across polygon, line, and point feature extraction.
  arXiv  Detail & Related papers  (2025-06-19T03:51:47Z)
• EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis [0.31077024712075796]
  We introduce EarthScape, a novel, AI-ready multimodal dataset for surficial geologic mapping and Earth surface analysis.<n>EarthScape integrates high-resolution aerial RGB and near-infrared (NIR) imagery, digital elevation models (DEM), multi-scale DEM-derived terrain features, and hydrologic and infrastructure vector data.<n>As a living dataset with a vision for expansion, EarthScape bridges the gap between computer vision and Earth sciences.
  arXiv  Detail & Related papers  (2025-03-19T18:23:48Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [64.58959634712215]
  Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface.<n>Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding.<n>To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
  arXiv  Detail & Related papers  (2025-01-10T18:59:42Z)
• Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes [2.273531916003657]
  We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in rainfall data across India from 1990 to 2015. Our work paves the way for sophisticatedtemporal anomaly detection methodologies in climate science, contributing to better climate preparedness and response strategies.
  arXiv  Detail & Related papers  (2024-11-12T12:24:48Z)
• FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [56.0640340392818]
  We introduce a framework, FREE, that enables the use of varying features and available information to train a universal model.<n>The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem.<n>Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines.
  arXiv  Detail & Related papers  (2023-11-17T00:53:09Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)
• Semi-Automated Segmentation of Geoscientific Data Using Superpixels [4.035753155957697]
  Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy. Inspired by the concept of superpixels, we propose a deep-learning based approach to segmentized survey data into regions with similar characteristics.
  arXiv  Detail & Related papers  (2023-03-20T19:21:46Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)

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.