Introduction: Hydrologic DROUGHT in the sense of deficient river flow is defined as the periods that river flow does not meet the needs of planned programs for system management. DROUGHT is generally considered as periods with insignificant precipitation, soil moisture and water resources for sustaining and supplying the socioeconomic activities of a region. Thus, it is difficult to give a universal definition of DROUGHT. The most well-known classification of DROUGHTs is based on the nature of the water deficit: (a) the meteorological DROUGHT, (b) the hydrological DROUGHT, (c) the agricultural DROUGHT, (d) the socio-economic DROUGHT. Perhaps the most widely used model is the ARIMA model for predicting DROUGHT. The two general forms of ARIMA models are non-seasonal ARIMA (p, d, q) and multiplicative seasonal ARIMA (p, d, q)×(P, D, Q) in which p and q are non-seasonal autoregressive and moving average, P and Q are seasonal autoregressive and moving average parameters, respectively. The other two parameters, d and D, are required differencing used to make the series stationary. The differencing operator that is usually used in the case of non-stationary time series. The aim of the study is to predict hydrological DROUGHT using time series analysis in the small forest watershed. . . .

DROUGHT prediction is an important item in realm of hydrometeorology and hydrology, and selection of suitable meteorological variables for DROUGHT prediction is a goal in recent studies. In this paper, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’ s time series and the well-known statistical machine learning methods, Support Vector Machine (SVM), is proposed to predict DROUGHT class based on Standardized Precipitation Index (SPI) in some seasonal scale scenario in the main watersheds of Tehran. In current study, ground weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) was applied in prediction models based on data from 1975 to 2005 in the main watershed of Tehran. Regarding to the amount of predictors, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’ s time series and target time series and the well-known statistical machine learning methods, support vector machine (SVM), is applied to predict SPI class. One of the important issue in this research is use of different variables, for example regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in classification and prediction of SPI and it is suitable and applicable. The predicted SPI in winter and autumn are more accurate than the other scenarios.

Optimum utilization of water resources in the country requires improving the accuracy of FORECASTING and estimation time of DROUGHT. One of the most important issues in monitoring and predicting DROUGHT is choosing an appropriate index for the area. In the present study, besides determining two indices of SPI and CZI in two scales of short-term and medium-term using the precipitation of two rain gauge stations in a period of 43 years (1972-2015) located in Abiyek City, the DROUGHT FORECASTING was performed using the Direct Multi-Step Neural Network in six time ahead. The Kappa- Cohen statistic used in order to review the consistency of quality classes between the predicted and observed values. The results of using this network in this study indicated an acceptable performance and capability of this network to estimate the DROUGHT using the two scales of SPI and CZI and predict some earlier steps of weather DROUGHT. Based on results of the weighted Kappa statistic showed that with increasing the prediction step, the similarity between the prediction amounts and the observed amounts in quality classes of DROUGHT decrease in two indices of SPI and CZI. So, by increasing the time scales (from 3 to 9 month), the similarity increases. The results of the prediction with the two mentioned indices and in different scales showed that Ziaran station seems more appropriate because it is located in the center of the area. Therefore, choosing an appropriate station in prediction issues helps improve the models significantly. Finally, this research can be useful in predicting the time of DROUGHT at least for the next six months, and help water planning and water resources managers in macro level in the country.

DROUGHT is a temporary and recurring meteorological event, originating from a lack of precipitation over an extended period of time. The success of DROUGHT preparedness and mitigation depends on timely information about DROUGHT onset and FORECASTING. This information may be obtained through continuous DROUGHT monitoring, which is normally performed using DROUGHT indices. DROUGHT is an unpleasant, naturally occurring event caused by climate change that directly affects societies through changing their access to water resources. Among the numerous indices for DROUGHT intensity rating, the EDI and SPI have widespread applications. The SPI was computed by fitting a probability density function to the frequency distribution of the monthly precipitation records of each station. A DROUGHT event is considered to occur at a time when the value of the SPI is continuously negative and ends when the SPI becomes positive. The computation of the SPI DROUGHT index for any location is based on the long-term precipitation record (at least 30 years) cumulated over a selected time scale. This long-term precipitation time series is then fi tted to a gamma distribution, which is then transformed through an equal probability transformation into a normal distribution. Positive and negative SPI values respectively indicate wet conditions (greater than median precipitation), and dry (lower than median precipitation). In most cases, the probability distribution that best models observational precipitation data is the Gamma distribution. Unlike most other DROUGHT indices, the EDI in its original form is calculated with the daily. The resulting EDI value represents standardized value for currently utilizable water resources, considering the continued dry period. If a negative DEP continues for more than 1day, the addition period of EDI will increase as long as the continued days. This variable addition period is limitless. The nature of genetic programming allows the user to gain additional information on how the system performs, i. e., gives insight into the relationship between input and output data. The GP is similar to genetic algorithm (GA) but unlike the latter, its solution is a computer program or an equation as against a set of numbers in the genetic algorithm. So, GP is more attractive than traditional GA for problems that require the construction of explicit models. The GP thus transforms one population of individuals into another one, in an iterative manner by applying operators. In evolutionary computation, it can distinguish between three different types of operators which are named crossover, reproduction, and mutation. M5 model tree approach is based on the principle of information theory that makes it possible to split the multi-dimensional parameter space and generate the models automatically according to the overall quality criterion. It allows for variation in the number of models created. The splitting in the M5 modal tree approach follows the idea of decision tree, but instead of the class labels, it has linear regression functions at the leaves, which can predict continuous numerical attributes. Model trees generalize the concepts of regression trees, which have constant values at their leaves. Therefore, they are analogous to piece-wise linear functions (and hence nonlinear). Computational requirements for model trees grow rapidly with increase in the dimensionality of the data set. Model trees learn efficiently and can tackle tasks with very high dimensionality. The major advantage of model trees over regression trees is that model trees are much smaller than regression trees and regression functions do not normally involve many variables. This research used precipitation data on two basins in Hamedan and Lorestan Provinces to calculate the SPI and EDI indices for monitoring DROUGHT. The genetic programming model and M5 model trees were used to predict the occurrence of DROUGHT in these two basins. It was found these models had good capability in predicting DROUGHT and enjoyed high accuracy in solving prediction problems. Another advantage of these models is that they use simple equations for predicting the phenomena under study. In the best-case scenario, the coefficients of determination for the EDI index in the M5 model trees and in the genetic programming model were 0. 97 and 0. 95, respectively. Moreover, the coefficients of determination for the SPI index in the M5 model trees and in the genetic programming model, in the best-case scenario, were 0. 93 and 0. 83, respectively. This suggests the M5 model trees are more accurate compared to the genetic programming model and enjoy relative superiority because they are simpler and more understandable than the genetic programming model.

DROUGHT is one of the important natural disasters that may happen in any climate conditions. Since DROUGHT is inevitable phenomenon, therefore familiar with that natural disaster is very important for reliable water management. DROUGHT prediction system design is one of the efficient ways that it can minimize the DROUGHT damages. In this research for predicting the coming DROUGHT, genetic algorithm and conjoined model of neural network-wavelet is used for analyzing standardized precipitation index. The results show that genetic algorithm and conjoined model of neural network-wavelet is more satisfactory than genetic algorithm and neural network.

Introduction: DROUGHT is a complicated phenomenon that arises from lack of rain and increasing of temperature. It may happen in all climates. Unlike flood, finding the beginning, duration and ending of DROUGHT is so difficult. It may last for weeks and months to recognize that really the DROUGHT has happened or not.Although DROUGHT happens fewer than other phenomenon in comparing with other natural disasters, but has dedicated the first rank from the view point of people’s life and financial damages…

FORECASTING DROUGHT is a challenging endeavor due to various underlying factors and mechanisms. Thus, the need for robust and precise FORECASTING models is paramount. In this study, a method that utilizes the wavelet neural network and spatial proximity data derived from satellite images to enhance the accuracy of DROUGHT forecasts is presented. This technique applies satellite-based precipitation and evapotranspiration data to calculate DROUGHT indices. It then uses the wavelet neural network approach to forecast DROUGHT intensity in different months of the subsequent year. To better discern random fluctuations from actual DROUGHT signals and enhance forecast accuracy, we utilize spatial proximity data from satellite images to forecast DROUGHT at the East Isfahan climate station. Our findings validate the capability of the wavelet neural network approach to forecast DROUGHT with a reasonable degree of accuracy. Also, leveraging neighboring data can potentially improve FORECASTING precision, as evidenced by a correlation of 0. 675 between the target and predicted values.