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The teleconnection modeling of hydro-climatic events is a complex problem with highly uncertain circumstances. In contrast to the classic fuzzy logic methods, by using the Z-number in addition to the constraint of information, and by evaluating the data reliability, it is possible to characterize the degree of ambiguity of data. In this regard, this study investigates the performance of the Z-number-based model (ZBM) in prediction of classified monthly precipitation (MP) events of two synoptic stations in Iran (up to five months in advance). To this end, the sea surface temperature (SST) of adjacent seas was used as a predictor. The suggested model, by using Z-number directly and applying fuzzy Hausdorff distance to determine weights of if-then rules, predicted MP events of both the stations with over 70% confidence. Analysis of the results in the test step showed that the ZBM compared to the traditional fuzzy approach improved the results by 69% for Kermanshah and 112% for Tabriz. Overall, the Z-number concept by assessing events reliability can be used in various sectors of water resources management such as decision-making and drought monitoring. [ABSTRACT FROM AUTHOR]

The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes critical to have an advanced understanding of rainfall patterns in the basin, setting the motivation of this study. In this research, the mean monthly meteorological data of six synoptic stations of Urmia Lake basin were used (including relative humidity, temperature, minimum–maximum temperature and pressure) and large-scale atmospheric circulation indices (Southern Oscillation Index, North Atlantic Oscillation, Western Mediterranean Oscillation, Mediterranean Oscillation-Gibraltar/Israel and Mediterranean Oscillation-Algiers/Cairo) and sea surface temperatures of the Mediterranean, Black, Caspian, Red seas and Persian Gulf in the period 1988–2016. Various combinations of these variables used as input to the M5 tree and random forest models were selected by Relief algorithm for each month in three scenarios including atmospheric circulation indices, meteorological variables and combination of both. After the implementation of two models with three different scenarios, the evaluation criteria including correlation coefficient (R), mean absolute error and root-mean-square error were calculated and the Taylor diagram for each model was plotted. Our results showed that the M5 tree model performed superior in January, February, March, April, June, September, November and December, while the random forest model did in the remaining months. In addition, the indications of this study showed that the combination of atmospheric circulation indices and meteorological variables used as input to the models mostly constituted improved results. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models. [ABSTRACT FROM AUTHOR]