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.
