Simulation of Rainfed Wheat Yield Using Drought Indices by Employing Artificial Neural Network, Random Forest and Support Vector Regression (Case Study: Saqqez City)

Drought can mainly affect agriculture, especially rainfed agriculture due to its high dependence on precipitation, thereby compromising food security and social protection. In this study, the correlation between SPI and SPEI drought indices with rainfed wheat yield of 5 filds in Saqqez city during the period of 2001-2020 was investigated with neural network, random forest and support vector regression. TRMM precipitation and CRU evapotranspiration were used to calculate SPI and SPEI drought indices. The AquaCrop model was calibrated with observational data and then filds performance was simulated with the AquaCrop model for the period 2001-2020. The average yield of the filds was evaluated with the average yield of the entire Saqqez city, and the results showed that the data simulated with the model had a good correlation (R2=0.90) with the average rainfed wheat yield of Saqqez city. The results of evaluating the relationship between SPI and SPEI indices with rainfed wheat yield showed that the neural network and random forest method with a significant probability of 95% (P-value=0.0) and an explanatory coefficient of more than 0.70% and a high value of Nash Sutcliffe index And a small amount of underestimation has had a good estimate of the rainfed wheat yield and there is a significant relationship between SPI and SPEI drought indices with the rainfed wheat yield in the study area.