Zoning the possibility of landslides downstream of Sanandaj Dam

1-Introduction

The purpose of this study is to select the best model and identify landslide risk areas in the downstream basins of Sanandaj Dam. Every year, mass movements in the region cause damage to roads, power lines, natural resources, farms and residential areas, and increase soil erosion. Kurdistan province, with its mostly mountainous topography, high tectonic activity, diverse geological and climatic conditions, has the most natural conditions for mass movements. According to the available statistics, this province is the third province in terms of landslides after Mazandaran and Golestan. (Naeri, &Karami, 2018). The Gheshlagh River Basin is a mountainous region with a north-south trend. In terms of construction land, it is located on the structural zone of Sanandaj-Sirjan. The study area with an area of 970.7 square kilometers is located downstream of Sanandaj Dam. The city of Sanandaj is being studied within the region. Due to the type of climate and morphological processes, effective parameters are provided for landslides in the geography of the region.

2-Methodology

In this study, 9 effective factors for landslides, including slope, slope direction, fault distance, road distance, waterway distance, lithology, land use and precipitation were used. Using Google Landsat 8 ETM satellite imagery, Google Earth software identified 237 slip points. Then, the coordinates of the slip points were transferred to the Arc GIS software and a map of the landslide distribution area in this environment was prepared. Also, in this study, 89 non-slip points were prepared for use in the training and testing stages of Persephone neural network inside slopes less than 5 degrees. Artificial neural networks are made up of a large number of interconnected processing elements called neurons that act to solve a coordinated problem and transmit information through synapses. Neural networks begin to learn using the pattern of data entered into them. Learning models, which is actually determining their internal parameters, is based on the law of error correction. In this method, by correcting the error regularly, the best weights that create the most correct output for the network are identified. The neurons are in the form of an input layer, an output layer, and an intermediate layer. ahp includes a weighting matrix based on pairwise comparisons between factors and determines the level of participation of each factor in the occurrence of landslides. In this model, a large number of factors can be involved and the weight of each factor can be obtained using expert opinion.

3-Results

According to the results of the high-risk class neural network model, which occupies 31% of the basin area, it is the widest risk zone in the region. The middle class also accounts for more than 29 percent of the area, followed by the low-risk class. The results of the AHP model show that the middle class, with 32% of the area, has the highest dispersion in the region, the low-risk class and then the high-class are in the next position. The AHP model was used to prioritize the parameters affecting the landslide. The parameters of slope, lithology and land use play the most important role in the occurrence of landslides, respectively, and have the least role for slope direction, distance from fault and height. The results of the models used are consistent with the reality of the region's wide-risk hazards, and high-risk areas based on the models used are mostly located in the west and southwest of the basin. These areas correspond to the mountain unit and the steep slope. Based on the results of AHP model, the impact of human factors in the occurrence of landslides is weaker than the natural factors of the region and human factors play a stimulating and aggravating role in primary factors. Five methods for error detection were used to evaluate the models used

4-Discussion and conclusion

.Due to the sensitivity of unstable slopes in the region, any planning to change the use and construction that increases the weight of the load on unstable slopes should be done in terms of geomorphological and geological conditions of the region.

Keywords: hazard zoning, landslide, neural network, AHP. Sanandaj Gheshlagh Watershed