Water resources carrying capacity to achieve a sustainable ecosystem using support vector regression

Abstract

Abstract Agricultural water resources carrying capacity has been considered an important problem in recent decades. A comparison of the evaluation indicators of water resources indicated the variation levels of the stability. Machine Learning-Support Vector Regression (ML-SVR) was implemented to formulate the agricultural footprints. The obtained statuses of the water resources have always been characterized by agricultural deficit in the Hendijan plain, Khuzestan province, Iran. Experiments performed outperformed the classical model on both fitted values and the validation value. The results showed that the agricultural footprints from 2010 to 2020 in Iran kept steady with higher levels, while from 2014 to 2016 witnessed a significant decline compared with previous years. The predicted agricultural footprint for the recent 10 years continues to decrease in the semi-arid regions. The predicted results via support vector regression (SVR) showed that agricultural footprints from 2017 to 2020 will present a rising trend, meaning the situation of water crisis will be increasingly serious in the eastern parts of the central deserts.