ABSTRACT:The Southern Oscillation Index (SOI) is an acceptable scientific index for determining the strength of El Niño Southern Oscillation (ENSO) phenomenon. Because of the importance of reference evapotranspiration (ET 0 ) in determining crop water demand, this study was conducted to assess the impacts of different ENSO phases on ET 0 variability in some warm climates of Iran. For the estimation of ET 0 , the daily meteorological variables from a set of stations during a period of 50 years were used in an aerodynamic energy balance approach and the correlation between SOI and the estimated ET 0 values for two scenarios (with and without time lag) was constructed. Using Spearman, Pearson and Mann-Whitney approaches, the correlation coefficients (r) and the statistically significant relative differences between the mean ET 0 values and their corresponding variations in each phase were verified. The results of seasonal ET 0 showed that in 54% of the study sites, significant (P < 0.05) correlations between ENSO events and the ET 0 variations exist. In the monthly timescale, 88% of the significant SOI-ET 0 correlations experienced positive signs. In most of the cases, the spring and winter ENSO events influenced the ET 0 values one or two seasons after the occurrence of the ENSO. On average, the mean monthly ET 0 values during El Niño phases were 10.1 and 9.3% lower than the corresponding ET 0 values during La Niña and normal phases, respectively. On the contrary, the mean monthly ET 0 values during La Niña were 8.4% higher than that in normal phase. It was found that the degree of impact of ENSO on ET 0 variability is sensitive to the timescale of analyses. Furthermore, the ET 0 variations in warm arid sites were more sensitive to teleconnection impact of ENSO than the humid sites.

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The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R2) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.

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