Volume 10, Issue 3 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(3): 71-84 |
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Ara H, Gohari Z, Memarian H. Modeling the risk of advancing sand areas using expert algorithms and artificial intelligence. Journal of Spatial Analysis Environmental Hazards 2023; 10 (3) :71-84
URL: http://jsaeh.khu.ac.ir/article-1-3382-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3382-en.html
1- Assistant Professor, Department of Arid and Desert Management, Faculty of Desertology, Semnan University, Semnan, Iran. , ara338@semnan.ac.ir
2- PhD in Desertification, Semnan University, Semnan, Iran.
3- Associate Professor, Department of Watershed Rangeland, Faculty of Natural Resources and Environment, Birjand University, Birjand, Iran
2- PhD in Desertification, Semnan University, Semnan, Iran.
3- Associate Professor, Department of Watershed Rangeland, Faculty of Natural Resources and Environment, Birjand University, Birjand, Iran
Abstract: (2138 Views)
Introduction
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of the most important factors (David and Nicholas, 1994). Sand areas are one of the desert landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding and interaction with the environment and ultimately its sustainable management and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.
Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of the most important factors (David and Nicholas, 1994). Sand areas are one of the desert landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding and interaction with the environment and ultimately its sustainable management and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.
Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.
Type of Study: Research |
Subject:
Special
Received: 2023/06/26 | Accepted: 2023/11/25 | Published: 2023/09/23
Received: 2023/06/26 | Accepted: 2023/11/25 | Published: 2023/09/23
References
1. اکبری، مرتضی. 1382. ارزیابی و طبقه بندی بیابان زایی با تکنیک RS و GIS در منطقه خشک شمال اصفهان، پایان نامه کارشناسی ارشد بیابان زدایی، دانشگاه صنعتی اصفهان، دانشکده منابع طبیعی.
2. علوی پناه، سید کاظم. 1385. کاربرد سنجش از دور در علوم زمین، انتشارات دانشگاه تهران.
3. Bijaber, N.; El Hadani, D., Saidi, M., Svoboda, M. D., Wardlow, B. D., Hain, C. R., Rochdi, A.2018. Developing a remotely sensed drought monitoring indicator for Morocco. Geosciences, 8(2): 55.
4. Breckle, S.W.; Veste, M., Wucherer, W. 2001. Sustainable Land Use in Deserts. Springer, Germany.
5. David, S.G.T.; Nicholas, J.M., 1994. Desertification Exploding the Myth. Wiley, New York.
6. Chang, C. L.; Liao, C. S. 2012. Parameter sensitivity analysis of artificial neural network for predicting water turbidity. International Journal of Geological and Environmental Engineering, 6(10): 657-660.
7. Falaki, M. A.; Ahmed, H. T., Akpu, B. 2020. Predictive modeling of desertification in Jibia Local Government Area of Katsina State, Nigeria. The Egyptian Journal of Remote Sensing and Space Science, 23(3):363-370.
8. Feng, Y. 2017. Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules. International Journal of Geographical Information Science, 31(6): 1198-1219.
9. Florian, J.; Thomass, S., Thorsten, W., Gerhard, E.W. 2001. Arid rangeland management supported by dynamic spatially explicit simulation models. In: Breckle, S.W., Veste, M., Wucherer, W. (Eds.), Sustainable Land Use in Deserts. Springer, Germany
10. Jamali, A.2021. Improving land use land cover mapping of a neural network with three optimizers of multi-verse optimizer, genetic algorithm, and derivative-free function. The Egyptian Journal of Remote Sensing and Space Science, 24(3):373-390.
11. Janitza, S.; Tutz, G., Boulesteix, A. L. 2016. Random forest for ordinal responses: prediction and variable selection, Computational Statistics & Data Analysis, 96: 57-73.
12. Gad, A.; Lotfy, I. 2006. Use of remote sensing and GIS in mapping the environmental sensitivity areas for desertification of Egyptian territory. In: Proceedings of the Second International Conference on Water Resources and Arid Environment 2006, Riyadh, Kingdom of Saudi Arabia, 26–29 November 2006.
13. Goodin, D.G.; Anibas, K.L., Bezyennyi, M. 2018. Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape, International Journal of Remote Sensing, 36: 4702-4723.
14. Lu, D., Mausel, P., Brondizio, E., & Moran, E. 2004. Change detection techniques. International journal of remote sensing, 25 (12): 2365-2401.
15. Memarian, H.; Balasundram, S. K. 2013. Comparison between multi-layer perceptron and radial basis function networks for sediment load estimation in a tropical watershed. Journal of Water Resource and Protection, 4(10):870.
16. Memarian, H.; Balasundram, S. K., Tajbakhsh, M. 2013. An expert integrative approach for sediment load simulation in a tropical watershed. Journal of Integrative Environmental Sciences, 10(3-4): 161-178.
17. Mirzaei.N. ;Saraf,A., Application of data integration models in simulating river flow using large-scale climate signals, case study: Jiroft Dam watershed. Watershed engineering and management,4(13):672-689.
18. Principe, J.; Lefebvre, W. C., Lynn, G., Fancourt, C., Wooten, D. 2007. NeuroSolutions-documentation, the manual and on-line help,Version 5.05. NeuroDimension, Inc.
19. Pickard, B., Gray, J., & Meentemeyer, R. (2017). Comparing quantity, allocation and configuration accuracy of multiple land change models. Land, 6(3), 52.
20. Philips, Z., Bojke, L., Sculpher, M., Claxton, K., & Golder, S. (2006). Good practice guidelines for decision-analytic modelling in health technology assessment. Pharmacoeconomics, 24(4), 355-371.
21. Rumelhart, D. E.; Zipser, D. 1986. Feature discovery by competitive learning, Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations,567.
22. Yosefi, M.; Poorshariaty, R.2015. Suspended Sediment Estimation using Neural Network and Algorithms Assessment (Case Study: Lorestan Province).Journal of Watershed management research, 5(10): 85-97.
23. Wang, B., Waters, C., Orgill, S., Cowie, A., Clark, A., Li Liu, D., ... & Sides, T. (2018). Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecological indicators, 88, 425-438.
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