Geotourism
Mohhamadhosen Rezaei moghadam; Mohamadreza Nikjou; Kamran KHalilvalizadeh; Belvasi Imanali; Mehdi Belvasi
Abstract
Landslide is one of the natural hazards in mountainous regions that results in huge losses every year. Alashtar Doab watershed with mountainous terrains, uplands and different natural conditions has the potential for landslide. The purpose of this study is landslide hazard zoning using artificial neural ...
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Landslide is one of the natural hazards in mountainous regions that results in huge losses every year. Alashtar Doab watershed with mountainous terrains, uplands and different natural conditions has the potential for landslide. The purpose of this study is landslide hazard zoning using artificial neural network model in Alashtar Doab watershed. In order to preparing the map, first of all parameters of the landslide were extracted and then the layers were prepared and after that a landslide distribution map that was occurred in the basin was prepared and then by combining landslide influencing factors with landslide distribution map, the impact of each of these factors such as slope, aspect, lithology, rainfall, land use, distance from fault and stream in ArcGIS software were measured. In this research, artificial neural network model with error back propagation algorithm and sigmoid activation function was used. The final structure of the network consisted of eight neurons in the input layer, eleven neurons in the hidden layer and one neuron in the output layer. Network accuracy in the testing phase was calculated by 85.93 percentages. After optimization of the network structure, all area information was imported to the network. Based on landslide hazard zoning using artificial neural network model, 37.44, 45.7, 93.8, 49.32 and 76.6 percent of the area at risk is located in very low, low, medium, high and very high classes, respectively.
Climatology
MohammadHoseein Rezaei Moghaddam; Khalil Valizadeh Kamran; Mehdi Belvasi; Hoseein KheiriAstiyar; Sayad Asghari Saraskanroud
Volume 20, Issue 56 , August 2016, , Pages 127-148
Abstract
One of the most important procedures in the water sources studies is the estimation of the local distribution of precipitation in different time scales. The study of precipitation is a basic element in the water balance studies and is an important factor in the natural sources programs of each country. ...
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One of the most important procedures in the water sources studies is the estimation of the local distribution of precipitation in different time scales. The study of precipitation is a basic element in the water balance studies and is an important factor in the natural sources programs of each country. Also, because of the rain-evaluation stations deficiency and their discreteness, it is necessary to use a special model. Besides the interpolation of precipitation amounts of stations, this model should interpolate topography, moisture and the slope direction of precipitation. In this work, at first, some data were gathered, in one year. These data were connected with the precipitation and moisture of 9 synoptic stations and 31 rainevaluation stations. These stations were located in the Lorestan province. Second, using the least square method and with the help of Maple software, the relations between precipitation and moisture was extracted. Third, by using the Python programming language, these relations were linked into the GIS. Finally, by so doing, the digital precipitation modal was achieved. The results obtained from the digital precipitation model show that, the precipitation amounts are different from the measured data in the stations, from 0.02 to 11.6 mm. Also, to investigate the efficiency of the considered model, the data obtained from this model were compared with the precipitation data achieved from TRMM radar at 21 April 2010. The concluded result show that, the determination coefficients are 79 and 86% for the TRMM data and for the digital precipitation model, respectively
