Zusammenfassung

This document presents a systematic literature review analyzing machine learning (ML) models utilized for climate change prediction and climate risk assessment. The review synthesizes findings from 40 peer-reviewed studies published between 2015 and 2024, employing thematic analysis to categorize the literature into five major clusters: deep learning architectures for temperature and precipitation forecasting, ensemble methods for extreme weather event prediction, hybrid physics-informed neural networks, spatiotemporal models for sea-level rise and glacier dynamics, and ML-based frameworks for socioeconomic impact modeling. Key findings indicate that Long Short-Term Memory (LSTM) networks and Transformer-based architectures significantly outperform traditional statistical models in long-range climate forecasting, while gradient boosting methods excel in regional risk classification. Physics-informed neural networks are highlighted for their interpretability and effectiveness in data-scarce environments. The review identifies critical research gaps, including the need for model interoperability, uncertainty quantification, and the integration of socioeconomic factors. It suggests future research directions focusing on federated learning approaches and explainable AI frameworks to improve transparency and stakeholder trust in climate modeling efforts.

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