Abolghasem Amir Ahmadi; Mahnaz Naemi Tabar; Bahar Gholkar ostadi
Volume 4, Issue 11 , September 2017, , Pages 105-125
Abstract
Absract:
Introduction
Landslide is one of the natural phenomena causing many financial losses and casualties in Iran every year (Kamranzadeh, 2014: 101). This phenomenon occurs when the force of materials’ weight is higher than the shear strength of the soil shear force (Memarian et al. 2006: ...
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Absract:
Introduction
Landslide is one of the natural phenomena causing many financial losses and casualties in Iran every year (Kamranzadeh, 2014: 101). This phenomenon occurs when the force of materials’ weight is higher than the shear strength of the soil shear force (Memarian et al. 2006: 105). The Shannon entropy is a function of probability distribution and standard for measuring uncertainty in the information content of a parameter, and by considering occurrence frequency of subgroups of that parameter, it shows heterogeneity level. As a result, it calculates the effect of each parameter on the results of the system (Hosseinpour Mil Arghadan et al. 2014). Objectives of the present study are the selection of criteria and standards, preparation of digital factors layers, preparation of the landslide hazard zonation map, diagnosis of high risk points via the Shannon entropy, presentation of strategies appropriate for preventing possible risks and solutions to reduce damages in the study area. Bajgiran is the central district of Bajgiran County and a part Doulatkhaneh Rural District of Ghouchan Township. According to climate divisions, Bajgiran has a moderate mountainous climate. Geologically and structurally, it is a part of Kopeh Dagh Sedimentary Basin. In terms of stratigraphy, outcrops from the Jurassic rock units to the present era can be observed in the study area.
Materials and methods
In the present study, first of all factors affecting the occurrence of landslide including height, precipitation, slope, slope direction, slope shape, distance from the waterway, distance from the road, distance from the fault, land cover and lithology were identified as factors affecting the occurrence of landslides, and the mentioned maps were digitized in GIS. to this end, using the topographic map on a scale of 1:50000, the Digital Elevation Model Map (DEM), factors of slope degree, slope direction, slope shape, height level, distance from the waterway, and distance from the road were prepared. Using the land-use map on a scale of 1:25000, information layers of land use were extracted. To draw the lithological map, the distance from the fault of the geological map on a scale of 1:50000 was used. To draw the precipitation map, statistics of the rain gauge stations of five Daroungar, Mohammad Taghi Beig, Aman Gholi, Kikan, Hey Hey Ghouchan, and Bahman Jan Stations were used. The information content available in the decision matrix in entropy process is calculated via equation 1:
Equation 1: Ej = -K
Where Ej is the entropy value and Pi,j is the decision matrix.
Equation 2: Pij =
Where rij is the value or the special score assigned to each layer.
Equation 3: K= (lnm)-1
Where k is the fixed coefficient and m is the number of landslides.
After the formation of the decision matrix and extraction of the value of Ej, the value of Vj can be calculated via equation 4:
Equation 4: Vj = 1- Ej
Where Vj is the deviation degree of uncertainty.
And finally, to calculate the final weight of all factors (Wj), equation 5 is employed.
Equation 5: Wj =
To prepare the final map, equation 6 is used:
Equation 6:
Where Hi is the landslide hazard occurrence coefficient, Wj is the final weight of all factors, rij is the weight of each factors (Moghimi et al. 2012: 82).
Results and discussion
After converting criteria into integers and the formation of the initial matrix, the value of Pij was calculated via equation 1 and the value of K was calculated via equation 2. To calculate Ej for each criterion, equation 2 was used. The results are indicated table 2. In this equation, the value of E which is a function of n, for each n where Pi is equal, the value of E becomes maximum which is statistically calculated via probability distribution of Pi. Then, uncertainty or degree of deviation of each criterion (dj) obtained from the fraction of the value of Ej from 1 were calculated per each indices effective on landslides of the study area (table 2). After that, using equation 5, the weight of each parameters used in the entropy matrix of landslides (Wj) including height (0.02113), precipitation (0.031142), shape of slope (0.0116110), slope (0.011342), distance from the waterway (0.045161), distance from the road (0.113401), distance from the fault (0.099871), land use (0.997110), and lithology (0.095148) were obtained. Therefore, the regional model of the landslide hazard degree in the area was obtained via equation 6. Hi is the landslide hazard degree in the area (equation 7).
Conclusion
The aim of the present study was to prioritize factors affecting the occurrence landslides and zone their sensitivity in Bajgiran Region via the Shannon entropy. The results of the study shows that the most important factors affecting landslides in the study area are slope layers, slope direction, lithology, distance from the fault, and height. After weighting parameters and formatting the entropy matrix, the zonation mapping were conducted. To this end, information layers were prepared in Arc GIS and converted into Raster formats. With regard to zoning maps obtained from the entropy model, 15 landslides have occurred in the area among which 9 landslides have occurred in a high risk zone (42%), 4 landslides in a moderate risk zone (31%), and 2 landslides in a low risk zone (27%). Regarding the factor of slope, it can be said that the most landslides have occurred in slopes with 60%. It may because the lack of the soil-formation process prone to slippery movements. In case of the factor of slope direction, the most landslides have occurred in northern domains and in heights with 1600 m high. This results is compatible with the faults and calcareous, marl, and Pyura Chilensis organizations of the area. The results of the present study also show that the entropy model has appropriate performance in identifying risk areas and their zonation. In addition, the results can be used in decision making and management of land use and urban planning.
