Drought analysis with machine learning methods

Environmental factors, which directly affect the living beings cause the formation of many natural disasters. One of the most important of these disasters is drought. The effect of the drought on the water resources also affects many things in the way of living life. From the point of human life, diminution in water resources, may pose a significant threat. Drought does not appear suddenly, hence it is possible to predict and take necessary measures before it exists. In order to predict the drought, various drought indices are used to determine the drought phenomenon. A great deal of research has been made to estimate the drought values that have changed dramatically so far. In this study, the 116 - year Palmer Drought Severity Index (PDSI) values of Kayseri province were modeled using machine learning methods in order to predict future PDSI values. In this context, one, three and six months period of drought values were predicted. The success rate of the predictions constructed using support vector machines (SVM) and K-nearest neighbors (KNN) algorithms was evaluated statistically. This study indicates that machine learning methods provide a significant contribution to the solution of hydrological problems.