Predicting Drought Based on SPI and RDI on 3- month Time Scale Using Artificial Neural Networks and Decision Tree

A variety of significant natural phenomena affect water resources, especially in agriculture area. If they occur in a vast range of climates, they result in drought. Drought forecasting plays an important role in the design and management of water resources, determining the water requirement of the plant, etc. These days, there are several methods available to predict drought. In recent years, the use of new computer models has expanded. For instance, Artificial neural network (ANN) has been successful in modeling and predicting processes that lack explicit solutions and relationships for accurate identification and description. Additionally, the decision tree (DT), as one of the mathematical models, generates the law by examining the parameters from one to the other, ultimately gaining insights from the available statistical data. In this study, the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) on a 3- month time scale are computed by exploiting ANN and DT, the prediction of drought according to SPI and RDI on a 3-month time scale in Shiraz synoptic station is performed for the period 2010- 2019 and the results are investigated

Keywords:

Artificial neural network , Decision Tree , Drought , SPI , RDI.