A multi-source data-driven framework for probabilistic flood risk assessment using cascade machine learning models: case study in the Sichuan Basin.

Taking into account the annual temperature deviation of 0.7 °C within the region during the baseline period (1990–2014), the final model predicts a maximum temperature difference of approximately 1.2 °C among the three locations by the end of this century, indicating an increasing trend in temperature disparities between stations.

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