Spatiotemporal precipitation modeling by ai based ensemble approach

Abstract

This study aimed at time-space estimations of monthly precipitation via a two-stage modeling framework. In temporal modeling as the first stage, three different AI models were applied to observed precipitation data from seven stations located in the Turkish Republic of Northern Cyprus (TRNC). In this way two scenarios were examined, each employing a specific inputs set. Afterwards, the outputs of single AI models were used to generate ensemble techniques to improve the performance of the precipitation predictions by the single AI models. To end this aim, two linear and one nonlinear ensemble techniques were proposed and then, the obtained outcomes were compared. In the second stage, for estimation of the spatial distribution of precipitation over whole region, the results of temporal modeling were used as inputs for the IDW spatial interpolator. The cross-validation was finally applied to evaluate the overall accuracy of the proposed hybrid spatiotemporal modeling approach. The obtained results in temporal modeling stage demonstrated that the non-linear ensemble method revealed higher prediction efficiency.