Saeid Khosrobeigi Bozchelui; Arash Malekian; Alireza Moghaddam Nia; Shahra,m Khalighi
Abstract
Flood is one of the most devastating natural disasters, causing financial and human losses each year. At the same time, many rivers in Iran's watersheds lack complete and accurate statistics and information. On the other hand, estimating the flow of floods is one of the most important factors for the ...
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Flood is one of the most devastating natural disasters, causing financial and human losses each year. At the same time, many rivers in Iran's watersheds lack complete and accurate statistics and information. On the other hand, estimating the flow of floods is one of the most important factors for the design and implementation of water structures. In such cases, one of the appropriate solutions to estimate the maximum flow rate with different return periods is flood analysis. In order to conduct the present study, 55 hydrometric stations with a common statistical period of 20 years were considered to perform the work after the statistical deficiencies were eliminated. Then, based on the distribution of the third type of Pearson logo with the lowest error rate and the highest number of first rank as the most suitable fit function, the amount of discharge in different return periods was estimated. The following information was collected on the types of physiography, land use, climate and geology variables. After collecting information about all independent variables using Gamma test, the most important variables affecting the maximum instantaneous flow, including area, drainage density, maximum 24-hour rainfall and watershed environment, were selected and modeled using methods. Random forest modeling and support vector modeling were performed and their efficiency was determined based on statistical indicators With an efficiency coefficient of 74 to 83%, the error of 3.05 to 32.11 m3 and the coefficient of explanation of 76 to 91 are more accurate than the random forest model.
Haniyeh Rezaie; Sharareh Pourebrahim; Mohammad Karimadini
Abstract
Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study ...
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Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study has aimed to introduce Google Earth Engine to evaluate the pattern of land changes during 2006- 2021 and predict the pattern of future changes by using an integrated model based on Cellular automata and Markov chain using Google Earth Engine system. Three Landsat images (2006, 2014 and 2021) were classified using the support vector machine classifier method, and were simulated using the integrated model of cellular automata and Markov chain. In order to evaluate the accuracy of the predicted map of 2021, the classified map of the same year was applied. The accuracy of classified and simulated maps was Kno=0.812, Klocation=0.816, Kstandard=0.786 respectively. Evaluation of the land use/cover changes shows that between 2006 and 2035, the buildup areas will reach from 4839.01 hectares to 7199.76 hectares with increasing of 2360.75 hectares. These results indicate the necessity of land use planning principles. Simulation models can reduce the risks of long-term decision-making in land use management and Google Earth Engine can reduce the time and cost for classification and satellite image processing.
Mohammad Ali Saremi Naeini
Abstract
Rapid population growth and the need to provide the necessities of life has caused human beings to change land use widely. In many cases these changes have been accompanied by disruption of balance in nature and providing the possibility of soil erosion. This study was carried out to investigate the ...
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Rapid population growth and the need to provide the necessities of life has caused human beings to change land use widely. In many cases these changes have been accompanied by disruption of balance in nature and providing the possibility of soil erosion. This study was carried out to investigate the effect of land use changes on increasing the intensity of wind erosion. Landsat satellite imagery and implementation of supervised classification by using SVMs were used to prepare land use map. Ground surface temperature was calculated, as one of the effective factors in wind formation, with single channel and split window algorithms in different land uses. In order to make connections between land use changes and wind erosion, effective climatic parameters such as temperature, relative humidity and maximum wind speed were investigated. The highest surface temperature was observed in poor and non-vegetation areas, and the lowest temperature was estimated in highlands and rangelands. Results showed an increase in average temperature as well as maximum and minimum absolute temperature and decrease in relative humidity. Over the past decade, the maximum wind speed in the study area showed a significant increase, and it has increased from eight m/s in 1990 to more than 20 m/s in the last decade. This shows that land use change by removing vegetation cover, as well as the uncontrolled increase of human constructions and creating thermal islands in the last 30 years, has a significant impact on the emergence of climate change and increasing wind speed.
Elham Kakaei Lafdani; Ali Reza Moghaddam Nia; Azadeh Ahmadi; Heydar Ebrahimi
Abstract
This study aimed to examine the influence of pre-processing input variables by Gamma Test on performance of Support Vector Machine in order to predict the suspended sediment amount of Doiraj River, located in Ilam Province from 1994-2004. The flow discharge and rainfall were considered as the input variables ...
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This study aimed to examine the influence of pre-processing input variables by Gamma Test on performance of Support Vector Machine in order to predict the suspended sediment amount of Doiraj River, located in Ilam Province from 1994-2004. The flow discharge and rainfall were considered as the input variables and sediment discharge as the output model. Also, the duration of the model training period was determined through GT. Thereafter, in order to carry out the influence of pre-processing input variables on performance of model, the suspended sediment was predicted using SVM model while no pre-processing has been done on its input variables and the results were compared to each other. Results show the performance of the GT-SVM model in the test phase with minimum RMSE was equal to 0.96 (ton/day) and the maximum coefficient of R2 was equal to 0.98 between the predicted and actual values, was better than SVM model.
