Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia

This study advances an integrated Bayesian support vector machine-based two-step factorial analysis (abbreviated as BSVM-TFA) method for revealing the influences of human activities on water demand. The developed method can capture complex nonlinear relationships between human activities and water demand by calibrating SVM hyperparameters through Bayesian optimization, which helps prevent overfitting. BSVM-TFA can also identify the individual and interactive effects of multiple factors on water demand and screen key influencing factors. The BSVM-TFA is then applied to Central Asia, and the results show that by 2050, water demand would range from 75.66 × 109 m3 to 113.23 × 109 m3 under different scenarios, indicating an uncertainty of about 33.18 % driven by human activities. The key factors influencing water demand in Central Asia are GDP and agricultural irrigation efficiency (AIE), with a total contribution of 47.98 %; the water demand would be reduced by 16.42 × 109 m3 with low-growth GDP and increasing AIE.