Kiyoumars Roushangar; sadegh Abdelzad
Abstract
IntroductionPredicting the maximum temperature changes is very important and has become increasingly important due to the many effects it has on water resources, agriculture and the environment. By forecasting the temperature, one can be aware of future changes and make the necessary arrangements to ...
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IntroductionPredicting the maximum temperature changes is very important and has become increasingly important due to the many effects it has on water resources, agriculture and the environment. By forecasting the temperature, one can be aware of future changes and make the necessary arrangements to adjust its negative effects on water resources, agriculture and the environment. Therefore, modeling and forecasting the maximum temperature can be used as an important tool in the planning and management of natural, economic and industrial resources.Materials and methodsIn this article, the maximum temperature was modeled using the Long-Short-Term Memory (LSTM) method based on Discrete Wavelet Transform (DWT) and Complete Experimental Mode Decomposition (CEEMD) methods in two different climates (humid and semi-arid). For this purpose, the daily data of maximum temperature, minimum temperature, precipitation, and solar radiation were used from 2001 to 2020 of the synopic stations located in Siyahbisheh, Amol City in Mazandaran Province and Urmia City airport in West Azarbaijan Province. It was determined that in the semi-arid region, the parameters of maximum and minimum temperature two days before, and maximum and minimum temperature one day before, as well as the minimum temperature and solar radiation of the same day, and in the humid region, the parameters of maximum temperature two days before, and maximum and minimum temperature one day before, as well as the minimum temperature and solar radiation of the same day were recognized as the superior model.Results and discussionThe results of the analysis of the models showed the capability and high efficiency of the method used in estimating the maximum temperature. On the other hand, the pre-processor methods improved the results. In the investigations, it was observed that the results of analysis based on wavelet transformation led to better results so that the DC evaluation criterion for the superior model in the semi-arid region of Urmia City went from 0.965 to 0.993 and in the humid area of Amol City increased from 0.926 to 0.970 and the RMSE criterion in Urmia Airport decreased from 1.943 to 0.896 and in Siyahbisheh from 2.595 to 1.648.ConclusionThe results showed an increase in DC evaluation criteria and a decrease in RMSE for the synoptic station of Urmia Airport by 2.74% and 53.87%, respectively, and by 4.80% and 35.50% for the Siyahbisheh Amol Synoptic Station, respectively. This again shows that wavelet conversion has the greatest effect in improving the performance of the LSTM model and the selected models have high capability and efficiency in estimating the maximum temperature. According to the results of the sensitivity analysis, it was determined that the temperature parameter of the previous day is the most influential in estimating the maximum daily temperature for two regions with different climates (humid and semi-arid).
Kiyoumars Roushangar; Saman Shahnazi
Abstract
In most water resources studies, the bed load transport rate is considered as a constant proportion of total load due to the difficulty and costs associated with measuring of it, which is not reasonable due to the high variability of this ratio. In this study, data collected from 19 coarse-grained rivers ...
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In most water resources studies, the bed load transport rate is considered as a constant proportion of total load due to the difficulty and costs associated with measuring of it, which is not reasonable due to the high variability of this ratio. In this study, data collected from 19 coarse-grained rivers in the United States were employed to predict bed load, total load transport rates and the ratio of bed to total sediment load using Support Vector Machine which is a branch of intelligent methods. Next, the results were compared and evaluated with classical methods. Results showed that this method has a very high performance compared to the classical methods and performance criteria in predicting the bed to total sediment load ratio has acceptable results. In addition, the modeling showed that the ratio of average velocity to shear flow velocity and the Froude number is the most effective parameters in predicting bed load, total load and the ratio of these.
