arXiv AI
Jul 10
AI-integrated models for assessing agricultural resilience
arXiv:2607. 07759v1 Announce Type: new Abstract: Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems.
Real-Time Climate Risk Assessment for Supply Chain Resilience: A Data-Driven Nowcasting Framework for Colombian Agriculture
arXiv:2608. 09846v1 Announce Type: new Abstract: This paper presents a methodological framework for real-time climate risk assessment using data-driven nowcasting techniques to enhance supply chain resilience in Colombian agricultural contexts.
Related stories
arXiv AI
Jul 22
LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks
arXiv:2603. 04818v3 Announce Type: replace Abstract: Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings.
arXiv Machine Learning
Jul 27
From Seasonality to Semantics: Benchmarking a Hybrid Probabilistic Forecasting System for Roadblocks in Bolivia
arXiv:2607. 21785v1 Announce Type: cross Abstract: Roadblocks in Bolivia are a social conflict phenomenon with devastating economic impacts, estimated at losses equivalent to 4% of the national Gross Domestic Product.
arXiv Machine Learning
Aug 6
Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka
arXiv:2608. 04023v1 Announce Type: cross Abstract: Sri Lanka's fisheries sector is important for jobs and food supply.
arXiv Machine Learning
Jun 26
Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
arXiv:2602. 17683v3 Announce Type: replace Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture.
arXiv Machine Learning
Jun 30
When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
arXiv:2606. 29248v1 Announce Type: new Abstract: Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated, so supply disruptions quickly drive prices up.
arXiv Machine Learning
Aug 4
Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
arXiv:2608. 01864v1 Announce Type: cross Abstract: Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning.
arXiv AI
Jun 3
Auditable Climate Risk Intelligence from Fragmented ESG Data: Deterministic Orchestration and Imbalance-Aware Learning for Scope 1-3 Validation
arXiv:2606. 02604v1 Announce Type: cross Abstract: ESG and climate risk data remain fragmented across heterogeneous Scope 1, Scope 2, and Scope 3 reporting environments, while conventional validation pipelines lack provenance aware auditability, hidden drift detection, and reproducibility oriented governance.
arXiv Machine Learning
Jun 5
Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.
arXiv Machine Learning
Jun 15
Can Machine Learning Forecast Rice Yields in Data-Constrained Settings? Satellite Climate Data, National Crop Statistics, and Lessons from Sierra Leone
arXiv:2606. 13959v1 Announce Type: new Abstract: Sierra Leone's agriculture operates with almost no data-driven decision support, and no published machine learning study has examined the country's crop yields.
arXiv Machine Learning
Jul 21
Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting
arXiv:2607. 17507v1 Announce Type: new Abstract: Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference.
arXiv Machine Learning
Aug 4
Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams
arXiv:2608. 00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated.