Spatiotemporal precipitation modeling by artificial intelligence-based ensemble approach

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 Artificial Intelligence (AI) models were applied to observed precipitation data from 7 gauges located at Northern Cyprus. In this way 2 different input scenarios proposed, by employing different input combinations. Afterwards, the outputs of single AI models were used to generate ensemble techniques to enhance the precision of modeling by the single AI models. For this purpose, 2 linear and 1 non-linear methods of ensembling were designed and afterwards, the results were evaluated. 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 Inverse Distance Weighting (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 technique provided more accurate results. Results of spatial modeling stage indicated that IDW scheme is a good choice for spatial estimation of the precipitation. The overall results show that the combination of temporal and spatial modeling tools could simulate the precipitation appropriately by serving unique features of both tools.