Application of probability decision system and particle swarm optimization for improving soil moisture content
2021
Panpan Zhu | Hossein Saadati | Majid Khayatnezhad
Drought is one of the natural disasters having the highest degrees in comparison to the other natural disasters in terms of rate, intensity, incident duration, region expansion, life losses, economic damages, and long-term effects. Hence, the prediction of drought as a meteorological phenomenon should be evaluated to determine the groundwater exploitation strategies in agriculture. The present study aims at investigating the impact of the drought duration and severity on soil moisture supplement for agricultural activities in Baghmalek plain, Khuzestan province, Iran. For this objective, a non-dimensional index of precipitation depth was defined for quantifying the drought characteristics. Furthermore, marginal distribution functions, correlation coefficients and joint functions were incorporated to a probabilistic decision-making framework to predict the variables in different return periods from 2-year to 100-year periods. Results showed that t copula was the best function for constructing the multivariate distribution in the study area based on the goodness-of-fit tests. Moreover, soil moisture content in the root zone achieved by the predetermined amounts of precipitation could be increased in the seasonal average. HIGHLIGHTS A new probabilistic framework was developed for sustainable water allocation.; Fuzzy particle swarm optimization was employed for generating the optimal solution.; A daily time-step model was simulated using mathematical formulation of crop growth process.; Developed framework increased the water use efficiency in the cropping pattern of Baghmalek as the study area.;
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