Evaluation of the Performance of CANFIS, MLPNN, MLR and M5 Models in the Simulation of Meteorological Drought Index (Case Study: Kermanshah Synoptic Station)
Drought is one of the most destructive phenomena in the world, especially in Iran. The timely prediction of drought and its severity can make it easier to take the necessary measures to combat this phenomenon. Different methods have been proposed to predict droughts; however, what matters is which method can make the predictions more accurate. Many researchers have compared the CANFIS model with other models such as neural networks and linear regression Malik and Kumar (2020b); Malik et al(2020a); Malik et al (2019), but it has not been tested against the M5 tree model. In this study, CANFIS, M5, MLPNN and MLR models have been used to predict drought in Kermanshah synoptic station, to enhance the accuracy of drought prediction by using a variety of modeling methods in addition to the influential variables of the SPI index.
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