An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

Abstract

Drought prediction serves as an early warning to the effective management of water resources to avoid the drought impact. The drought prediction is carried out for arid, semi-arid, sub-humid, and humid climate types in the desert region. The drought is predicted using Standardized precipitation evapotranspiration index (SPEI). The application of machine learning methods such as artificial neural network (ANN), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN) for the drought prediction suitability is analyzed. The SPEI is predicted using the aforesaid machine learning methods with inputs used to calculate SPEI. The predictions are assessed using statistical indicators. The coefficient of determination of ANN, KNN, and DNN are 0.93, 0.83, and 0.91 respectively. The mean square error of ANN, KNN, and DNN are 0.065, 0.512, and 0.52 respectively. The mean absolute error of ANN, KNN, and DNN are 0.001, 0.512, and 0.01 respectively. Based on results of statistical indicator and validations it is found that DNN is suitable to predict drought in all the four types of desert region.