Extended AbstractIntroductionDrought monitoring entails the simulation of indices which are categorized into single and combined types. Historically, simulations have predominantly relied on single indices, including Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), resulting in limited research on drought simulation using combined indices (i.e. MSPI and SPTI), particularly in conjunction with combined models. Over the years, several single models have been developed for simulating individual drought indices. For instance, the Autoregressive Integrated Moving Average (ARIMA) model has been applied to simulate drought indices like Standardized Precipitation Index (SPI) and Standard Index of Annual Precipitation (SIAP). Additionally, models such as Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) have been used for simulating indices like SPI, DI, SIAP, and SHDI. Recent studies suggest that combined models outperform single models. Wavelet ARIMA ANN (W-2A) and Wavelet ANFIS combined models to simulate the single drought index SPEI. Other researchers have developed combined models such as ARIMA-LSTM, Wavelet-ARIMA-LSTM, Wavelet-ARIMA-ANN and LSTM-CM to simulate single drought indices SPI, DI, SIAP. Despite the progress in developing drought simulation models, including single models and particularly combined models, their application has primarily focused on individual indices. Historically, simulations have predominantly relied on single indices, resulting in limited research on drought simulation using combined indices, particularly in conjunction with combined models. This study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) to test new methods of hybrid models for their ability to drought simulations based on the new combined index SRGI, employing the combined models W-AL and W-2A.Materials and MethodsDrought simulated in the Alashtar sub-basin between 48, 15 east longitude and 33, 54 north latitudes, covering an area of 811 square kilometers from 1991 to 2020, utilizing individual indices such as SPI, SRI, SGI, and the combined index SRGI. The study area encompasses the Karkheh River basin. Both single models (ARIMA, LSTM, ANN) and combined models (W-AL and W-2A) were employed for this purpose. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) were used to evaluate the performance of the models. Also, relative frequency and error distribution charts were used to evaluate and compare the results of the models.Individual indices were calculated based on fitting the best cumulative probability function to monthly precipitation, monthly discharge, and monthly water table data, respectively for indices SPI, SRI, and SGI, and then inversely transforming to a N (0,1). The SRGI index is a combination of two drought indices, SGI and SRI (Feng et al., 2020). For this purpose, the copula function is used to obtain the best joint probability distribution function governing precipitation and water table data. The selection of the best copula function was done through the Kolmogorov-Smirnov K-S test at a significant level of 5%. In the current research, four copula functions of Frank, Clayton, Gamble and Joe were used.The process of building the combined models includes the analysis of the time series of the studied drought index, using DWT and decompose into two series named approximate and partial. Then, the approximate and detail series modeled by ARIMA and ANN respectively, in W-2A model and ARIMA and LSTM, respectively, in W-AL model. Results and DiscussionThe results demonstrate that the combined models W-AL and W-2A exhibit higher accuracy across all indices, both individual and combined, compared to single models ARIMA, LSTM, and ANN. The RMSE ranges for the combined models were 0.44 to 0.71, while for single models, they ranged from 0.47 to 1.54. Specifically, model W- AL displayed superior accuracy across all individual indices, with RMSEs of 0.44, 0.62, and 0.59, in contrast to model W-2A, which yielded RMSEs of 0.49, 0.71, and 0.63. However, W-AL's performance lagged behind W-2A for the combined SRGI index, with respective RMSEs of 0.64 and 0.61. Thus, the simpler model yielded more acceptable results in simulating the composite index.ConclusionAmong all the combined and individual models, the combined models perform better in simulating drought, based on all indices, compared to the individual models. Therefore, it can be said that combined models are more suitable for simulating and monitoring drought compared to individual models. However, the performance of the two combined models, W-2A and W-AL, in simulating the combined SRGI index is different. The performance of the simpler W-2A model is better than the more complex W-AL model, with RMSE values of 0.61 and 0.64, respectively. Therefore, in combined indices, despite the complexity of their computational process, there is not necessarily a need to use a more complex combined model. Overall, the use of combined models is recommended for monitoring various types of indices, especially drought based on combined indices such as SRGI. The major objectives of this study are: (1) to use hybrid models Wavelet-ARIMA-LSTM (W-AL) and Wavelet-ARIMA-ANN (W-2A) methods to predict monthly drought. (2) To analyze drought characteristics in Alashtar basin based on the new combined drought index, SRGI. It is expected that the research results will help to provide decision support which in turn will help in planning adaptative measures to reduce drought impacts and provide decision support for disaster prevention.

Introduction: Increasing water demand and water pollution due to the development of agricultural, urban and industrial activities have caused environmental problems all over the world. The significant increase in water pollution and the diversity of various urban, agricultural and industrial pollutants made the qualitative management of water resources inevitable. Short-term and long-term accurate forecasts of river quality parameters are essential for designing hydraulic structures, irrigation planning, optimal utilization of reservoirs and environmental planning. Given the stochastic characteristics of the hydrological events, forecasting the future status of surface waters is always associated with uncertainties. The purpose of the present study was to investigate the performance of two types of artificial neural networks, namely MLP and GMDH, combined with discrete Wavelet transform (DWT), to forecast two important quality parameters, electrical conductivity (EC) and sodium adsorption ratio (SAR) at Zayandeh-Rood River in 1, 2 and 3 months ahead. Material and methods: In this study, water quality data (EC and SAR) of Zayandeh-Rood River at Zaman Khan Station was used from 1363 to 1384. From 21 years of data, 15 years (approximately 70%) were used for training and 7 years (30%) were used to test the developed models. Two types of mother Wavelet dmey and db4 were evaluated. Statistical parameters such as RMSE and R2 were used to evaluate the performance of the models. Results and discussion: The results showed that the use of discrete Wavelet transform improves the performance of the models. Various combinations of input data (various delays) and two types of mother Wavelets were evaluated. The results showed that Wavelet-MLP and Wavelet-GMDH hybrid models outperform single MLP and single GMDH models at all forecasting intervals. The results of the single MLP and GMDH models were only effective in forecasting SAR one month ahead but practically could not forecast two and three months later. In the EC parameter, the MLP and GMDH models performed better. Also, the results showed that the use of annual time lags does not increase the accuracy and in some cases even reduces it. The study of the types of mother Wavelets also showed that the dmey Wavelet is the most suitable Wavelet type to forecast EC and SAR qualitative parameters. The comparison between Wavelet-MLP and Wavelet-GMDH models showed the relative superiority of the former model. By increasing the forecast period from one month to three months ahead, the accuracy of the models decreased. This decrease in precision was higher in forecasting SAR parameter, e. g. in the one month forecast, R2 was 0. 936 and in the 3 months ahead forecasts it was reduced to 0. 516. In the EC parameter, the R2 fell to 0. 641 in 3 months ahead forecasting. Conclusion: The results of this study can be used as a basis for future planning for water quality. It is suggested that the model presented in this study should be considered in other rivers. Also, the combination of other artificial intelligent models such as ANFIS and SVM with Wavelet transform can be evaluated.

Rainfall-runoff process is one of the most important and complex phenomena in the hydrological cycle and therefore different views have been presented for modeling the phenomenon. Obviously, the recognition of the behavior of the catchment can play an important role in selecting the appropriate model as well as saving time on the simulation. Previous studies have shown that the multi-linear models have an acceptable performance in the case of watersheds which usually have a regular rainfall pattern. In this study, the multilinear Wavelet-M5 model was introduced and the rainfallrunoff process in the Aji Chay catchment was investigated. At first, the main rainfall and runoff time series were decomposed to several sub-time series by the Wavelet transform to overcome its non-stationarity. Then the obtained sub-time series were imposed as input data to M5 model tree to forecast the runoff values and also the results were compared to the other models (i. e. ANN, M5 and WANN) by the root mean squared error and determination coefficient criteria. The results showed that the performance of the proposed hybrid Wavelet-M5 model increased up to 69% compared to the sole M5 model tree for the Aji Chay catchment.

The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden layer and output layer of the network; meanwhile, the Wavelet function parameters are randomly assigned and kept fixed during the training process. Besides the simplicity and speed of the proposed one-step algorithm, the experimental results verify the performance of the proposed method in terms of final model accuracy and computational time.

Background: Cardiovascular disease is the most common disease of the century and is considered as a major cause of mortality and heart failure in industrial and semi-industrial societies. Using electrical signals produced by the heart muscle have an important role in the recognition and diagnosis of heart diseases.Methods: In this study, the signal recorded via vectorcardiography using an electrode arrangement called Frank, and electrocardiography were performed; a limited number of signals available in the database of the National Metrology Institute of Germany (Physikalisch-Technische Bundesanstalt or PTB) were also used. At the end, the data in each field vectorcardiography and electrocardiographic were assessed and compared. In order to make better use of time and optimize signal analysis, feature extraction operation was performed in the Wavelet domain; and then, reducing the characteristics and classification were performed using support vector machine technique.Findings: There were the accuracy of 83.18% and the validity of 99.06% in vectorcardiography leads and the accuracy of 75.44% and the validity of 97.16% in electrocardiographic leads.Conclusion: Based on support vector machine classification system, the properties of the Frank system leads tended to better results than conventional 12-leads electrocardiogram (ECG).