Analysis of Different Modern Methods and Technologies in Typhoon Track Prediction

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically consistent representations of atmospheric dynamics, they still struggle to resolve localized convection, rapidly evolving storm systems, and rare, high-intensity precipitation events. This study systematically reviews recent advances in Artificial Intelligence (AI) for extreme rainfall prediction published between 2020 and 2026, using a transparent literature search strategy, predefined screening criteria, quality assessment, and a structured literature review matrix. The review synthesizes evidence from a final evidence base of 139 studies across several dimensions, including deep learning (DL) methodologies, multimodal data fusion, forecasting horizons, operational deployment, uncertainty quantification, and emerging intelligent forecasting paradigms. The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations. Despite notable progress, persistent challenges remain, including data scarcity, class imbalance, model generalization, physical consistency, uncertainty estimation, transferability, and the lack of standardized evaluation frameworks. This review provides a unified synthesis of current methodologies. It identifies key research gaps, highlighting the need for evaluation frameworks that address physical plausibility, uncertainty quantification, reliability, transferability, and operational relevance. The findings indicate that future extreme rainfall prediction systems should advance beyond accuracy-focused evaluation toward integrated, trustworthy, uncertainty-aware, and physically consistent forecasting approaches.

Quantification of Data and Model Uncertainty for Deep Learning-based Streamflow Prediction

Predicting North Atlantic Hurricane Tracks Through an Ensemble Approach Learned From Existing Methods

Since the 19th century, hurricane prediction has been a key factor in promptly warning the public and consequently reducing costs. However, forecasting methods persist with issues on prediction errors and consistency, and in response, forecasters have created ensemble approaches to better refine the current methods and eliminate those two issues. We propose a real-time ensemble approach, denoted as Real-time Integrated Threshold Ensemble (RITE), built upon existing methods to enhance prediction accuracy and stability (a “method" will refer to pre-existing technique, and an “approach" will refer to a technique built upon those methods). RITE involves averaging predicted locations from a selected number of stable and top-performing methods, determined based on their prediction errors, then combining the methods together across several consecutive years to form an ensemble approach for the following year. We determine the best number of top methods per year that optimizes an objective function based on prediction errors. We apply RITE to predict hurricane tracks using data provided by the National Hurricane Center on North Atlantic hurricanes and assess its uncertainty through bootstrapping. Our results demonstrate that RITE can provide stable and accurate predictions, in contrast to the individual methods.

Typhoon multi-task prediction based on displacement residual learning

The proposed residual prediction method effectively solves the problem of insufficient utilization of environmental field features in short-term typhoon prediction and provides efficient and accurate deep learning technical support for typhoon disaster early warning.

Comparative Benchmarking Study of Deep Learning Models for Geomagnetic Storms Prediction

Geomagnetic storms have been known to have a substantial impact on the performance of satellites, communication systems, navigation systems, and other space weather-based systems. This research is concerned with developing a data-driven forecast of geomagnetic activity using deep learning and traditional machine learning models. This study focuses on comparing deep learning and machine learning models for predicting the Disturbance Storm Time (Dst) index through solar wind and geomagnetic parameters. For the analysis, an hourly dataset for 2000-2024 has been utilized resulting 219,168 observations. From these, eight parameters including magnetic field components, solar wind speed, geomagnetic indices, and past Dst values have been selected. Additionally, Random Forest, XGBoost, LightGBM, and persistence were employed as traditional baseline models. The performance measures considered included RMSE, MAE, R², and Pearson correlation, alongside training time, inference time, number of parameters, and a feature ablation study. Among the evaluated deep-learning architectures, LSTM achieved the best average performance across the five independent seeds. The RMSE of LightGBM and persistence baselines was 3.77459 nT and 4.63338 nT, respectively. The ablation analysis shows that AE, Kp10, and solar-wind variables substantially influence forecasting performance, whereas historical Dst and a longer 48-hour window do not necessarily improve the reported error. Overall, the results have shown that it is possible to model nonlinear temporal dependencies in geomagnetic observations using recurrent architectures. However, the use of traditional baselines along with repeated-seeds and ablation tests makes the evaluation more thorough.

A Comprehensive Survey of Satellite-Based Wildfire Indicators and Spatiotemporal Modeling Approaches: Past, Present, and Future

Wildfires pose increasing environmental and socio-economic risks, particularly in climate-sensitive and tropical regions, necessitating reliable satellite-based monitoring and predictive frameworks. This study presents a comprehensive survey of satellite-derived wildfire indicators and spatiotemporal modeling approaches, covering their historical development, current methodologies, and emerging research directions. We review major active fire and hotspot datasets derived from MODIS, VIIRS, and related platforms, along with key environmental drivers such as vegetation indices, meteorological variables, and land-surface with a specific case study for the Indonesian region. Modeling approaches are synthesized from classical statistical regression and time-series analysis to contemporary machine learning and deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models. The analysis highlights the transition toward multi-source data integration and spatiotemporal deep learning frameworks capable of capturing complex wildfire dynamics. Finally, we identify future research challenges, including hybrid physical–AI modeling, uncertainty quantification, and scalable real-time wildfire intelligence systems. This survey provides a structured reference for researchers and practitioners seeking to advance satellite-based wildfire monitoring and prediction.