MACHINE LEARNING–INTEGRATED BAYESIAN MODELING FOR CLIMATE RISK PREDICTION IN PAKISTAN

Abstract

This study proposes a Machine Learning–Integrated Bayesian modeling framework for climate risk prediction in Pakistan, aiming to enhance the accuracy, interpretability, and uncertainty quantification of extreme weather forecasting. Pakistan is highly vulnerable to climate-induced hazards such as floods, heatwaves, and droughts, which necessitate advanced predictive systems capable of capturing nonlinear climatic interactions and probabilistic uncertainty. Traditional forecasting approaches are limited in handling complex environmental dynamics, while standalone machine learning models often lack uncertainty estimation. A quantitative computational approach was employed using historical climate datasets from 2004–2024, including temperature, rainfall, humidity, and river flow variables. Machine learning algorithms such as Random Forest, Gradient Boosting, and Support Vector Machines were integrated with Bayesian inference techniques to develop a hybrid predictive model. Model performance was evaluated using RMSE, MAE, accuracy, AUC, and Bayesian uncertainty metrics. The results revealed that the proposed ML–Bayesian hybrid model outperformed conventional statistical and standalone machine learning models, achieving the highest predictive accuracy and lowest error rates. The Bayesian component significantly improved uncertainty quantification, enhancing the reliability of climate risk predictions. Rainfall and river flow were identified as the most influential predictors of extreme climate events in Pakistan. The study concludes that integrating machine learning with Bayesian modeling provides a robust, scalable, and interpretable framework for climate risk prediction. The proposed approach can support early warning systems, disaster preparedness, and evidence-based climate policy formulation in Pakistan