Volume 8, Issue 3 (12-2021)
Journal of Spatial Analysis Environmental Hazards 2021, 8(3): 157-170 |
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Mosaffaei Z, Jahani A, Zare Chahouki M A, Goshtasb Meygoni H, Etemad V. Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park. Journal of Spatial Analysis Environmental Hazards 2021; 8 (3) :157-170
URL: http://jsaeh.khu.ac.ir/article-1-3069-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3069-en.html
Zahra Mosaffaei1 
, Ali Jahani 2, Mohammad ALi Zare Chahouki3 , Hamid Goshtasb Meygoni1 , Vahid Etemad3
, Ali Jahani 2, Mohammad ALi Zare Chahouki3 , Hamid Goshtasb Meygoni1 , Vahid Etemad3
1- Collage of Environmen
2- Collage of Environmen ,ajahani@ut.ac.ir
3- University of Tehran
2- Collage of Environmen ,
3- University of Tehran
Abstract: (5197 Views)
Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park
Zahra Mosaffaei1, Ali Jahani2*, 3MohammadAli ZareChahouki, 4Hamid GoshtasbMeygoni, 5Vahid Etemad
1 Masters of Natural Resources Engineering, Environmental Sciences, College of Environment, Karaj
*2Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj.
3 Professor, Department of Restoration of arid and mountainous regions, University of Tehran, Karaj
4 Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj
5 Associate Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj
Abstract
Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.
Keywords: Modeling, Artificial Neural Network, Environmental Hazards, National Park, Vegetation
Zahra Mosaffaei1, Ali Jahani2*, 3MohammadAli ZareChahouki, 4Hamid GoshtasbMeygoni, 5Vahid Etemad
1 Masters of Natural Resources Engineering, Environmental Sciences, College of Environment, Karaj
*2Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj.
3 Professor, Department of Restoration of arid and mountainous regions, University of Tehran, Karaj
4 Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj
5 Associate Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj
Abstract
Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.
Keywords: Modeling, Artificial Neural Network, Environmental Hazards, National Park, Vegetation
Type of Study: Research |
Subject:
Special
Received: 2019/10/6 | Accepted: 2021/05/29 | Published: 2022/01/8
Received: 2019/10/6 | Accepted: 2021/05/29 | Published: 2022/01/8
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