پيش بيني درازمدت بارش با استفاده از خوشه بندي سيگنال هاي هواشناسي با توجه به تغييرات بارش به روش K-Means اصلاح شده (مطالعه موردي: پيش بيني بارندگي استان سيستان و بلوچستان)

LONG TERM PRECIPIDATION FORECASTING USING CLIMATE SIGNALS CLUSTERING WITH RESPECT TO PRECIPITATION VARIATIONS WITH MODIFIED K-MEANS METHOD (CASE STUDY: PRECIPIDATION FERECASTING OF SISTAN-BALOUCHESTAN PROVINCE)

Studying climatic variations and finding different hydro-logical variables, such as precipitation, can be very useful in the prediction of these variables. Some recent research has tried to explain the relation between large scale climate signals, like Sea Surface Temperature (SST), and some hydrological variables, such as precipitation. In this paper, a novel method for clustering is presented, which is called "Modified K-Means". This method is based on the ordinary K-Means method, but the framework of selecting the cluster centers is modified in a way wherein clustering SST and its relation with precipitation are considered simultaneously. This model tries to find the relationship between climatic signals and precipitation variations. 20 rain gauges in the Sistan & Baluchestan province in Southeastern Iran have been considered in the case study. The use of the Modified K-Means method for showing the relation between precipitation in various seasons and temporal-spatial variations of SST is an innovative aspect of this paper.