Prediction of regional water and land resource carrying capacity using support vector machines supported by Monte Carlo coupled black-winged kite optimization algorithm

Study region Heilongjiang Province in Northeast China. Study focus Studies on the carrying capacity of water and land resources (WLRCC) provide a critical basis for achieving the efficient use of these resources in the region. Traditional forecasting methods only calculate specific values at a single point in the future, failing to reflect the uncertainty of future water and land resource carrying capacity. Furthermore, the classification criteria for water and land resource carrying capacity are neither comprehensive nor accurate. To address these issues, this study employs a combined objective-subjective approach to establish classification criteria and proposes a forecasting method based on the coupling of Monte Carlo simulation with the Black Kite algorithm to optimize support vector machines, thereby resolving the issue of one-dimensional forecasting results for regional water and land resource carrying capacity. New hydrological insights for the region The carrying capacity of water and land resources in Heilongjiang Province will continue to show an overall upward trend from 2030 to 2040. Concurrently, the standard deviation—a measure of system variability—will increase significantly, with Jiamusi, Shuangyashan, and Qitaihe exhibiting the most pronounced increases, with standard deviation growth rates reaching 288%, 283%, and 198%, respectively. The findings of this study can provide guidance for the management and development of regional water and land resources.