Singapore

Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir’s metadata, reconciling DG’s invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.

AAAI 2026

HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

feature modulation

reservoir inflow prediction

hydrology

domain generalization

adversarial training

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Landmine contamination poses a significant threat in post-conflict regions, endangering civilian populations even decades after hostilities end. Demining efforts are resource-intensive, and prioritizing areas for clearance is particularly challenging in regions with widespread contamination. This paper presents a country-wide Digital Twin for landmine risk assessment, addressing key challenges: (a) modeling of a country-wide Digital Twin, (b) data integration, enrichment and augmentation, and (c) efficient machine learning (ML) training. The system is deployed in Cambodia premises, where it streamlines the development of ML workflows and achieves up to 91% accuracy in assessing risks of landmine areas.

Country-Wide Digital Twin for Humanitarian AI: The Case Study of Cambodian Mine Action

Financial risk assessment plays a vital role in accelerating the global energy transition and achieving the 1.5°C climate target. This study develops a three-stage machine learning framework to analyze the impact of fossil fuel price volatility on the weighted average cost of capital (WACC) for energy transition projects. First, a hybrid Long Short-Term Memory-Gaussian Regression Process (LSTM-GRP) model forecasts fossil fuel prices, followed by an LSTM-based inflation predictor. In the final stage, an AutoML (AutoGluon) regression model evaluates how variations in fossil fuel prices and inflation influence WACC across nine energy sectors and multiple global regions. 

The results show that the WACC in energy-importing regions like Europe (R²=0.67) and China(R²=0.64) is highly sensitive to price shocks, making their decarbonization pathways vulnerable to external market instability. Conversely, the energy-exporting countries in Middle East (R²=0.54) exhibit lower sensitivity, indicating a fiscal cushion that may reduce the economic impetus for transition. The findings demonstrate how energy price volatility shapes financing conditions and investment risks for renewable energy deployment. This approach integrates AI-driven forecasting with climate finance modeling, providing an interpretable and data-driven tool to support policy design, green investment strategies, and climate-resilient energy planning.

Fossil Fuel Prices and Capital Cost: A Machine Learning Driven Study on Energy Transition

Ecological studies on depression have explored the complex associations between environmental, social, and economic factors and mental health outcomes, but they often fall short of answering causal questions, which can limit the effectiveness of the interventions. To demonstrate the potential of machine learning in uncovering the causal mechanisms behind antidepressant prescriptions, which are overwhelmingly used to treat depressive disorders, we systematically examined 28,640 small geographical areas in England, each labeled with 27 socio-demographic and environmental indicators, as well as with the total annual per capita antidepressant prescriptions. Specifically, we employed a novel approach that integrates statistical analysis, machine learning, and domain expertise. Our results highlight the pivotal roles of ethnicity, green spaces, and dense urban structure as indirect causal links shaping antidepressant prescriptions, potentially mediated by hidden variables such as cultural attitudes and the likelihood of experiencing depressive symptoms. To validate our findings, we compared them with previous research, statistical modeling of ecological data, and results obtained through querying Large Language Models about causal links. Our causal inference approach showed efficacy in determining information flow directions and unveiling subtle relationships by considering a web of causation. Specifically, the results aligned for the most part with existing research, such as the complex associations with employment and economic conditions. Moreover, the findings also brought up some connections that warrant further research with individual-level data, including different effects from tree cover versus NDVI greenery.

Ecological Determinants of Antidepressants Prescriptions in England: Using Machine Learning for Causal Discovery

The widespread adoption of dynamic Time-of-Use (dToU) electricity tariffs requires accurately identifying households that would benefit from such pricing structures. However, the use of real consumption data poses serious privacy concerns, motivating the adoption of synthetic alternatives. In this study, we conduct a comparative evaluation of four synthetic data generation methods, Wasserstein–GP Generative Adversarial Networks (WGAN), Conditional Tabular GAN (CTGAN), Diffusion Models, and Gaussian noise augmentation, under different synthetic regimes. We assess classification utility, distribution fidelity, and privacy leakage. Our results show that architectural design plays a key role: diffusion models achieve the highest utility (macro-F1 up to 88.2%), while CTGAN provide the strongest resistance to reconstruction attacks. These findings highlight the potential of structured generative models for developing privacy-preserving, data-driven energy systems.

Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data

Weather forecasting offers an ideal testbed for artificial in-telligence to learn complex, multiscale physical systems. Traditional numerical weather prediction remains compu-tationally costly for frequent regional updates, as high-resolution nests require intensive boundary coupling. We introduce Multi-Resolution Graph Neural Forecasting (MR-GNF), a lightweight, physics-aware model that per-forms short-term regional forecasts directly on an ellipsoi-dal, multi-scale graph of the Earth. The framework cou-ples a 0.25° region of interest with a 0.5° context belt and 1.0° outer domain, enabling continuous cross-scale mes-sage passing without explicit nested boundaries. Its axial graph-attention network alternates vertical self-attention across pressure levels with horizontal graph attention across surface nodes, capturing implicit 3-D structure in just 1.6 M parameters. Trained on 40 years of ERA5 rea-nalysis (1980–2024), MR-GNF delivers stable +6 h to +24 h forecasts for near-surface temperature, wind, and precip-itation over the UK-Ireland sector. Despite a total com-pute cost below 80 GPU-hours on a single RTX 6000 Ada, the model matches or exceeds heavier regional AI systems while preserving physical consistency across scales. These results demonstrate that graph-based neural operators can achieve trustworthy, high-resolution weather prediction at a fraction of NWP cost opening a practical path toward AI-driven early-warning and renewable-energy forecasting systems.

MR-GNF: Multi-Resolution Graph Neural Forecasting on Ellipsoidal Meshes for Efficient Regional Weather Prediction

The accelerating pace of global warming, confirmed by the Intergovernmental Panel on Climate Change (IPCC), underscores the urgent need for continuous environmental monitoring and adaptive modeling frameworks. Therefore, this study explores AI-driven temperature forecasting across New Zealand using real observations from the national MetService network within the TAIAO data science programme. We evaluate a diverse set of models, from statistical baselines to deep neural networks, foundation models, and graph-based architectures, to assess their capacity for adaptive, spatially aware prediction. Our results show that graph-based representations substantially improve the modeling of spatial and temporal dependencies, while foundation models demonstrate robust generalization across diverse climatic regions. The integration of these paradigms produces forecasts that are both more accurate and more interpretable. The findings highlight the potential of adaptive AI frameworks to improve environmental monitoring, detect regional anomalies, and strengthen climate resilience strategies in New Zealand.

Environmental Monitoring in New Zealand: Exploring Spatiotemporal Relationships

Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution. Moreover, they tend to be object centric and struggle with complex and intricate scenes. Conversely, 3D-free methods can generate text-controlled views of complex, in-the-wild scenes using a pretrained stable diffusion model without the need for a large amount of 3D-based training data, but lack camera control. In this paper, we introduce a method capable of generating camera-controlled viewpoints from a single input image, by combining the benefits of 3D-free and 3D-based approaches. Our method excels in handling complex and diverse scenes without extensive training or additional 3D and multiview data. It leverages widely available pretrained NVS models for weak guidance, integrating this knowledge into a 3D-free view synthesis style approach, along with enriching the CLIP vision-language space with 3D camera angle information, to achieve the desired results. Experimental results demonstrate that our method outperforms existing models in both qualitative and quantitative evaluations, achieving high-fidelity, consistent novel view synthesis at desired camera angles across a wide variety of scenes while maintaining accurate, natural detail representation and image clarity across various viewpoints. We also support our method with a comprehensive analysis of 2D image generation models and the 3D space, providing a solid foundation and rationale for our solution. Furthermore, the proposed framework contributes to scenario generation and ecological visualization by enabling controllable, multi-view synthesis of natural and urban environments from limited imagery. This capability can support climate impact simulations and environmental narrative synthesis, aligning with recent advances in generative AI and foundation models for scientific and ecological applications.

3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance

Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation.

We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO$_2$ emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.

ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware

We present the Hierarchical AI-Meteorologist, an LLM-agent system that generates explainable weather reports using a hierarchical forecast reasoning and weather keyword generation. Unlike standard approaches that treat forecasts as flat time series, our framework performs multi-scale reasoning across hourly, 6-hour, and daily aggregations to capture both short-term dynamics and long-term trends. Its core reasoning agent converts structured meteorological inputs into coherent narratives while simultaneously extracting a few keywords effectively summarizing the dominant meteorological events. These keywords serve as semantic anchors for validating consistency, temporal coherence and factual alignment of the generated reports. Using OpenWeather and Meteostat data, we demonstrate that hierarchical context and keyword-based validation substantially improve interpretability and robustness of LLM-generated weather narratives, offering a reproducible framework for semantic evaluation of automated meteorological reporting and advancing agent-based scientific reasoning.

Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting

Rising sea levels pose an imminent hazard to coastal communities that depend on the sea for their livelihood. This paper presents a robust computational technique for forecasting tidal surge and resulting shoreline changes in the vulnerable Krishna-Godavari (KG) Delta, India. To ensure the reliability of decades-long projections and directly address the methodological shortcomings of prior work, this study is grounded in an multi-decadal historical dataset spanning 39 years (1977--2015), thus providing a scientifically justifiable temporal basis. The core methodology employs a deep-learning architecture: the Long Short-Term Memory (LSTM)network. To maximize its predictive efficiency, the LSTM's critical hyperparameters are rigorously optimized using the nature-inspired meta-heuristic : the K-means Whale Optimization Algorithm (WOA). This novel hybrid KWOA-LSTM approach achieves superior predictive performance over conventional models, demonstrated by a R2 score of 0.916 and a Mean Absolute Error (MAE) of 0.012. The predicted and forecasted surge values, computed by the optimized model, are visualized via the Google Earth Engine elevation map. Forecasts indicate significant saline intrusion and inundation will affect the KG Delta by 2060, posing a grave threat to the region's food security. The utilization of WOA for hyperparameter tuning, combined with the multi-decadal dataset, establishes a highly accurate and scientifically rigorous framework for long-term coastal hazard assessment and proactive planning.

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