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LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models

Haoran Li, Junqi Liu, Zexian Wang, Shiyuan Luo, Xiaowei Jia, Huaxiu Yao

TL;DR

This work addresses robust spatial-temporal environmental prediction under incomplete observations and distribution shifts by introducing LITE, a multimodal large language model that unifies environmental variables through semantic time-series descriptions and temporal trend images. LITE imputes missing data with a sparse mixture-of-experts, incorporates multi-granularity historical information to counter distribution shifts, and fuses multimodal representations using domain-guided prompts in a frozen LLM to predict targets. Across three real-world datasets (CRW-Temp, CRW-Flow, AGR), LITE achieves substantial RMSE improvements over strong baselines and demonstrates robustness to sensor outages and out-of-distribution regions, highlighting the method’s practical value for environmental decision-making. The approach illustrates the potential of combining foundation-model knowledge with domain-specific multimodal representations to improve environmental forecasting and policy relevance, with code and data publicly available.

Abstract

The modeling of environmental ecosystems plays a pivotal role in the sustainable management of our planet. Accurate prediction of key environmental variables over space and time can aid in informed policy and decision-making, thus improving people's livelihood. Recently, deep learning-based methods have shown promise in modeling the spatial-temporal relationships for predicting environmental variables. However, these approaches often fall short in handling incomplete features and distribution shifts, which are commonly observed in environmental data due to the substantial cost of data collection and malfunctions in measuring instruments. To address these issues, we propose LITE -- a multimodal large language model for environmental ecosystems modeling. Specifically, LITE unifies different environmental variables by transforming them into natural language descriptions and line graph images. Then, LITE utilizes unified encoders to capture spatial-temporal dynamics and correlations in different modalities. During this step, the incomplete features are imputed by a sparse Mixture-of-Experts framework, and the distribution shift is handled by incorporating multi-granularity information from past observations. Finally, guided by domain instructions, a language model is employed to fuse the multimodal representations for the prediction. Our experiments demonstrate that LITE significantly enhances performance in environmental spatial-temporal prediction across different domains compared to the best baseline, with a 41.25% reduction in prediction error. This justifies its effectiveness. Our data and code are available at https://github.com/hrlics/LITE.

LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models

TL;DR

This work addresses robust spatial-temporal environmental prediction under incomplete observations and distribution shifts by introducing LITE, a multimodal large language model that unifies environmental variables through semantic time-series descriptions and temporal trend images. LITE imputes missing data with a sparse mixture-of-experts, incorporates multi-granularity historical information to counter distribution shifts, and fuses multimodal representations using domain-guided prompts in a frozen LLM to predict targets. Across three real-world datasets (CRW-Temp, CRW-Flow, AGR), LITE achieves substantial RMSE improvements over strong baselines and demonstrates robustness to sensor outages and out-of-distribution regions, highlighting the method’s practical value for environmental decision-making. The approach illustrates the potential of combining foundation-model knowledge with domain-specific multimodal representations to improve environmental forecasting and policy relevance, with code and data publicly available.

Abstract

The modeling of environmental ecosystems plays a pivotal role in the sustainable management of our planet. Accurate prediction of key environmental variables over space and time can aid in informed policy and decision-making, thus improving people's livelihood. Recently, deep learning-based methods have shown promise in modeling the spatial-temporal relationships for predicting environmental variables. However, these approaches often fall short in handling incomplete features and distribution shifts, which are commonly observed in environmental data due to the substantial cost of data collection and malfunctions in measuring instruments. To address these issues, we propose LITE -- a multimodal large language model for environmental ecosystems modeling. Specifically, LITE unifies different environmental variables by transforming them into natural language descriptions and line graph images. Then, LITE utilizes unified encoders to capture spatial-temporal dynamics and correlations in different modalities. During this step, the incomplete features are imputed by a sparse Mixture-of-Experts framework, and the distribution shift is handled by incorporating multi-granularity information from past observations. Finally, guided by domain instructions, a language model is employed to fuse the multimodal representations for the prediction. Our experiments demonstrate that LITE significantly enhances performance in environmental spatial-temporal prediction across different domains compared to the best baseline, with a 41.25% reduction in prediction error. This justifies its effectiveness. Our data and code are available at https://github.com/hrlics/LITE.
Paper Structure (21 sections, 12 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 21 sections, 12 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Multimodal Large Language Model for Environmental Ecosystem Modeling
  4. Representation Learning from Semantic Time-Series
  5. Representation Learning from Temporal Trend Image
  6. Inference with Large Language Models
  7. Related work
  8. Experiments
  9. Experimental Setup
  10. Main Results
  11. Ablation Studies
  12. Performance Under Leaving-Sensors-Out Settings
  13. Analysis on Domain Generalization Ability
  14. Conclusion
  15. Appendix
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Overview of our proposed LITE model for environmental ecosystem modeling, which consists of (1) Transforming environmental data into natural language descriptions and line graph images; (2) multimodal representation learning; (3) multimodal fusion by LLM decoder.
  • Figure 2: Illustration of temporal trend image.
  • Figure 3: Experimental results under the leave-fixed-sensors-out (left two) and leave-random-sensors-out (right two) settings on CRW-Temp dataset.