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    ENN-SA: A novel neuro-annealing model for multi-station drought prediction
    (Pergamon-Elsevier Science Ltd, 2020) Mehr, Ali Danandeh; Vaheddoost, Babak; Mohammadi, Babak
    This paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales in a meteorology station with lack of data through the intelligent use of SPI series of the nearby stations as the model inputs. The capability of the hybrid model for multi-station prediction of meteorological drought was examined through the cross-validation technique for Kecioren station in Ankara Province, Turkey. To this end, the SPI-3, SPI-6, and SPI-12 at the station were modeled using the same indices of five nearby stations. In the first step, SVM was trained using different kernels in order to generate and classify a set of plausible multi-station prediction scenarios. Then, ENN was used to regress the SPI series at each scenario and finally, the SA component of the integrated model was utilized to improve the ENN efficiency. Various error and complexity measures were used to detect the models' performance. The results showed the ENN-SA is promising and efficient for multi-station SPI prediction.
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    A spatiotemporal teleconnection study between Peruvian precipitation and oceanic oscillations
    (Pergamon-Elsevier Science Ltd, 2020) Mohammadi, Babak; Vaheddoost, Babak; Mehr, Ali Danandeh
    Large-scale oceanic oscillations and their teleconnections with meteorological events are of great importance in macro-scale climatic studies. In this regard, this study investigates the spatiotemporal teleconnections between four oceanic oscillations, namely North Atlantic Oscillation (NAO), El Nino/Southern Oscillation (ENSO), Atlantic Multi-Decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO), against Peruvian precipitation patterns during the past 25 years (i.e., 1990-2015). For this purpose, variation in the precipitation pattern at monthly and annual scales as well as the Standardized Precipitation Index (SPI) time series at 1-, 3-, 12-, and 48-month time scales were evaluated at 10 meteorology stations across Peru. Pearson's correlation coefficient and mutual information between the oceanic oscillations and precipitation-born signals were calculated and spatially interpolated using the Kriging method. The results indicated the presence of three major climatic regions in the country. The NAO has the largest correlation with the monthly precipitation. However, the ENSO was found as the main climate driver of extremely wet and extremely dry conditions in the country. The results also demonstrated that the PDO has a higher impact on the annual precipitation pattern, particularly in the southern and eastern parts of the country.