► The materials and methods were combined. ► Full form of any abbreviation, were used when using first time in manuscript. ► The authors paid 325$ for editing the manuscript by a professional editing ...service.
The goal of the present research is forecasting the inflow of Dez dam reservoir by using Auto Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average (ARIMA) models while increasing the number of parameters in order to increase the forecast accuracy to four parameters and comparing them with the static and dynamic artificial neural networks. In this research, monthly discharges from 1960 to 2007 were used. The statistics related to first 42years were used to train the models and the 5 past years were used to forecast. In ARMA and ARIMA models, the polynomial was derived respectively with four and six parameters to forecast the inflow. In the artificial neural network, the radial and sigmoid activity functions were used with several different neurons in the hidden layers. By comparing root mean square error (RMSE) and mean bias error (MBE), dynamic artificial neural network model with sigmoid activity function and 17 neurons in the hidden layer was chosen as the best model for forecasting inflow of the Dez dam reservoir. Inflow of the dam reservoir in the 12 past months shows that ARIMA model had a less error compared with the ARMA model. Static and Dynamic autoregressive artificial neural networks with activity sigmoid function can forecast the inflow to the dam reservoirs from the past 60months.
Abstract Introduction: The limitation of the country's water resources and the aggravation of this limitation, which is caused by the continuous increase in the demand, has caused the maximum use of ...the available water resources and the increase in productivity and, as a result, the increase in production per unit area. The present research was conducted with the aim of evaluating the water quality of Dez River in terms of efficiency in pressurized irrigation systems. Methods: For quality assessment of Dez River water in terms of efficiency in pressurized irrigation systems, 7 hydrometric stations were selected including Sepid dashte Sezar, Sepid dashte zaz, Tange pange sezar, Tange pange bakhtiari, Dezful, Harmaleh, and Bamdezh. Qualitative data of Dez River water along a decade (2005-2014) were taken from Khuzestan water and power authority. Findings: The Qualitative analysis using piper diagram showed that water quality in Sepid dashte Sezar, Sepid dashte zaz and Tange pange sezar stations was calcium bicarbonate. Although, in Harmaleh and Bamdezh stations the most of samples were neutral. The results showed that salinity amount was increased from the upstream to the downstream of the river. Whereas, Bamdezh station had low to medium limitation at 95 percent of times for drip irrigation usage. Dez River had no limitation for infiltration due to suitable averages of SAR and EC. Besides, cations and anions coefficient of correlation was variable between 0.67 to 0.78 and -0.249 to 0.6 respectively. Chlorine in the river water from Harmaleh station toward the downstream of the river, had low to medium limitation for sprinkler irrigation usage. The most limitation of bicarbonate was observed at 97.5 percent of times in Sepid dashte Sezar station for using sprinkler irrigation. In addition, sodium amount in Harmaleh and Bamdezh stations compared to Dezful station increased 134 and 233 percent respectively. Langelier Saturation Index (LSI) was negative in the entire stations and calcium carbonate sediment won’t be created.
•Development of a hybrid soft computing model to predict the Runoff.•Preprocessing the signal of the time series of input variables using the Wavelet technique.•Design an MLPNN model and train it ...using the PSO technique.
A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science.Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDRm time series is achieved at 100. Then, the PACF(partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s.
Variation of rainfall origin in different parts of Iran along with latitude, distance and proximity to moisture sources, etc. causes rainfall behaviors such as intensity and continuity to have ...temporal and spatial changes .Using statistical methods and discharge data of Dez river, severe floods of this river were extracted to identify the effective factors in their creation.In this research, the information of all effective atmospheric levels has been analyzed using a combination of illustrative and appropriate maps.And Synoptic and thermodynamic properties of heavy cloud precipitation in Dez catchment At the same time, the synoptic patterns lead to the prolongation of the systems on the catchment And its effect on flow rate was identified and presented.The purpose of this study was to identify the pattern or patterns of durable rainfall systems in the Dez River Basin. There is a deep landing in the eastern Mediterranean that has caused the cold European air to fall in the west of the ship, as well as the transfer of moisture from the Arabian Sea and the Persian Gulf in the east. And the conditions for creating an ascent in the Dez River Basin are the most important elements of the pattern of heavy and durable rainfall in this basin. Water resources planning, prevention and timely information, and flood control and control are among the results that can be expected from this research.
The sedimentation phenomenon in multi-purpose dam reservoirs causes problems in agricultural water supply, hydropower generation, flood, and drought management.
In this regard, developing fast and ...straightforward methods to estimate the amount of sediment in dam reservoirs is of great importance. The present study aimed to evaluate a sediment rating curve model’s development using a time scale classification approach. Data of hydrometric Talezang Station and the Dez Dam Reservoir hydrography in southwestern Iran were used in this research. The data classified into monthly and seasonal time scales and groups of wet, dry, and flood seasons. Consequently, the results compared with the conventional model of the sediment rating curve (SRC) apply different precision criteria. The results indicate that the classification in monthly and seasonal time scales and wet, dry, and flood seasons improve the sediment load-rating model’s precision by 21, 16, and 3%, respectively. According to the reservoir hydrographic operation and the conventional model of sediment rating curve calculations, the volume of sedimentation during 40-year period is estimated 609 and 497 MCM, respectively. However, the data classification in monthly and seasonal time scales and wet, dry, and flood seasons led to the estimation of volumes of 539, 525, and 521 MCM, respectively. Therefore, it is concluded that temporal classification on a monthly scale can effectively improve the conventional sediment rating curve estimation and leads to an actual volume estimation.
Investir em avaliação de políticas educacionais tem sido uma constante nos governos cearenses dos últimos 20 anos. O intuito é aprimorar práticas metodológicas, gestão escolar e ampliar o desempenho ...no Sistema Permanente de Avaliação da Educação Básica do Ceará (SPAECE). Afinal, a avaliação pode contribuir para melhorias no processo de tomada de decisão dos gestores e, assim, qualificar o serviço público. O presente artigo se dedica a investigar o “Prêmio Escola Nota Dez” como metodologia de avaliação educacional e busca avaliar o seu efeito nas práticas de gestão escolar e melhoria dos indicadores, tendo como locus duas escolas municipais do Ceará, uma de Sobral e outra de Aquiraz. Estruturado sob o modelo metodológico quadripolar, o referencial teórico apoia-se nos conceitos de Max Weber para burocracia, meritocracia e eficiência na administração pública, fazendo uma relação com o pensamento de Ralph Tyler sobre avaliação por objetivos. Os resultados apontam que o Prêmio Escola Nota Dez favorece o desenvolvimento de ações de cooperação técnico-pedagógicas entre as escolas premiada e apoiada, e contribui para a melhoria do processo de gestão educacional e do desempenho dos estudantes.
This study investigates the performance of artificial intelligence techniques including artificial neural network (ANN), group method of data handling (GMDH) and support vector machine (SVM) for ...predicting water quality components of Tireh River located in the southwest of Iran. To develop the ANN and SVM, different types of transfer and kernel functions were tested, respectively. Reviewing the results of ANN and SVM indicated that both models have suitable performance for predicting water quality components. During the process of development of ANN and SVM, it was found that tansig and RBF as transfer and kernel functions have the best performance among the tested functions. Comparison of outcomes of GMDH model with other applied models shows that although this model has acceptable performance for predicting the components of water quality, its accuracy is slightly less than ANN and SVM. The evaluation of the accuracy of the applied models according to the error indexes declared that SVM was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties. By evaluating the results of the models based on the DDR index, it was found that the lowest DDR value was related to the performance of the SVM model.
Water scarcity poses a significant global challenge, particularly in developing nations like Iran. Consequently, there is a pressing requirement for ongoing monitoring and prediction of water ...quality, utilizing advanced techniques characterized by low implementation costs, shorter timeframes, and high accuracy. In the present study, the investigation and forecasting of the monthly time series of a single-variable river water quality index have been addressed using ten water quality parameters. Daily monitoring data from four stations in the Dez River from 2010 to 2020 have been utilized to obtain the river water quality index value from the dataset. The Shannon entropy method has been employed to assign weights to each water quality parameter. Utilizing the integrated autoregressive integrated moving average (ARIMA) model, which ranks among the most extensively employed models for time series forecasting, and five deep learning models including Simple_RNN, LSTM, CNN, GRU, and MLP, the water quality index for the following year is predicted. The performance of the prediction models is evaluated using RMSE, MAE, MSE, and MAPE as evaluation metrics. The results indicate that the ARIMA model performs worse than the deep learning models, with the MSE, RMSE, MAE, and MAPE values for this model being 81.66, 9.037, 6.376, and 6.749, respectively. The deep learning models show results close to each other, demonstrating similar statistical index values. The outcomes of this study assist relevant decision-makers in planning and implementing necessary actions to enhance water quality, particularly freshwater resources in rivers.
Semina: Ciênc. Agrár. Londrina, v. 44, n. 6, nov./dez. 2023 Semina: Ciênc. Agrár. Londrina, v. 44, n. 6, nov./dez. 2023
Semina. Ciências agrárias : revista cultural e científica da Universidade Estadual de Londrina,
01/2024, Letnik:
44, Številka:
6
Journal Article
Atrazine is one of the most widely used herbicides in the country, including Khuzestan province. The presence of this herbicide in aquatic environments poses a serious threat to human health and the ...aquatic ecosystem. Atrazine is an endocrine disruptor and is considered a carcinogen. The half-life of this herbicide in water and soil is between 20 and 50 days, that depends on the temperature and pH. Therefore, the present study aimed to determine the pesticide atrazine in agricultural drains and water of Karun and Dez Rivers. In order to measure the concentration of atrazine in agricultural effluent and Karun and Dez River, 15 stations upstream and downstream of the river were used. Determination of atrazine herbicide concentration was used by high performance liquid chromatography equipped with ultraviolet detector at maximum absorption wavelength. Construction of stock solutions was done by adding standard atrazine solution at concentrations of 1.5 and 0.5 ppm, 0.1, 0.05, and 0.05. Data analysis was performed using Excel software. The results showed concentration mean of atrazine in the downstream station (9, 11, 12 and 15) decreases compared to the upstream statin (1, 3, 5 and 6). Maximum concentrations of atrazine in summer, winter, autumn and spring were measured with 55.16, 53.02, 111.51, 49.86 µg/L, respectively, which is beyond the standard of the environment organization (MCL=3 µg/L). Liters and different seasons of sampling are about 94.56%, 94.34%, 94.13% and 93.98% higher than the WHO standards. The results showed that the amount of atrazine herbicide in most water samples of Karun River is beyond the standard and WHO and EPA regulations. The Karun River was observed. Maximum concentrations were observed in maxing point of Karoon and Dez Rivers (station 1) followed by Dehkoda agro – industry (station 6) upstream.