پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,236 |
| تعداد مقالات | 67,810 |
| تعداد مشاهده مقاله | 115,289,084 |
| تعداد دریافت فایل اصل مقاله | 90,031,831 |
Bayesian network application in socio-economic and ecological effects analysis in watershed management | ||
| Desert | ||
| دوره 28، شماره 2، اسفند 2023، صفحه 403-413 اصل مقاله (498.78 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2023.96217 | ||
| نویسندگان | ||
| A. Afzali1؛ K. Solaymani* 1؛ Sh. Rastgar2؛ A. Keshtkar3 | ||
| 1Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran. | ||
| 2Department of Range Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran. | ||
| 3International Desert Research Center (IDRC), University of Tehran, Karaj, Alborz, Iran. | ||
| چکیده | ||
| Natural resource management using appropriate management tools, is one of the critical issues in the watershed, especially in arid and semi-arid areas, land management and its associated resources will create a balance between economic and social needs and the sustainability of biological ecosystems. The present study, with the aim of watershed management and controlling sedimentation rates, evaluated socio-economic effects in the Damghan Rud watershed. The study area with an area of 5.66314 ha is located in Semnan Province. By choosing four economic and social activity variables, along with rangeland vegetation cover, were identified management options for the Damghan Rud basin. Based on the regional conditions and study objectives, four economic and social parameters the presence of surplus livestock, reliance of watershed residents on rangeland, Literacy of watershed residents’ level, and their participation percentage in water and soil resource conservation were determined as effective options for rangelands vegetation cover in the region. The sediment production rate at the output site of the selected basin at the Astana hydrometric station was investigated in the statistical period of 1995-96 to 2015-16, then by using the frequency distribution diagram of observational sediment, determined its classes. Then, with the implementation of the model were calculated the probabilities associated with each variable, and were evaluated the possible effects of the implementation of managerial options on the middle and target variables. The results showed that the option of reducing the reliance of watershed residents on rangelands to the minimum level led to a 15.3% increase in vegetation cover, a 5.5% reduction in river flow rate, and a 3.6% decrease in sediment production rate. Furthermore, the results showed that the Bayesian network models had a high capability and ability to express various dimensions of the issue and handle uncertainties within the system, and making them suitable tools for watershed resources management. | ||
| کلیدواژه‌ها | ||
| Watershed management؛ Bayesian network؛ Damghan Rud | ||
| مراجع | ||
|
Reference
Abbasi, A., K. Khalili, J. Bahmanesh, A. Shirzad, 2021. Estimation of daily river flow using intelligent models, case study: Mahabad River. Watershed Engineering and Management, 13(3); 614-624.
Ahmadi, H., M. Tahmourth, H. Mohammad Asgari, 2008. Using the fuzzy inference system in estimating suspended sediment. Journal of Science and Engineering Watershed of Iran, 2(5); 53-62.
Akhonipourhosseini. F., E. asadi, 2017. Bayesian networks, Gamma Test, Groundwater level, model Least Squares Support Vector Machine, Plain Ardebil. iranian journal of watershed management science, 11 (36); 33-42.
Akhonipourhosseini. F., M. A. Ghorbani, K. Shahedi, 2017. Using Shannon entropy in Bayesian network input preprocessing for time series modeling. Watershed Management Journal, 9(18); 178-189.
Agarwal, A. H., S. K. Mishra, J. K. Singh, 2006. Simulation of runoff and sediment yield using artificial neural networks. Biosys. Eng. 97(4); 597-613.
Anonymous, 1991. Technical Report on Sedimentology and sediment survey of Amir Kabir Dam. Surveys and Labs of Water Resources Institute. 113p. (In Persian)
Arabkhedri, M., 2021. The state of water erosion and sedimentation in Iran, statistical and comparative analysis. Journal of Strategic Research in Agricultural Sciences and Natural Resources. 6(2);139-156.
AWWA, 1997. Guidelines for implementing an effective integrated resource planning process. AWWA Research Foundation and American Water Work Assoication. 198 p.
Bashari, H., C. Smith, O.J.H. Bosch, 2009. Developing decision support tools for rangeland management by combining state and transition models and Bayesian belief networks. Agricultural Systems. 99; 23–34.
Basnyat, P., L.D. Teeter, K. Flynn, B.G. Lockaby, 2000. The use of remote sensing and GIS in watershed level analyses of non-point source pollution problems. Forest Ecology and Management. 128; 65-73.
Bhaduri, B., J. Harbor, B. Engel, M. Grove, 2000. Assessing watershed-scale, long-term hydrologic impacts of land-use change using a GIS-NPS model. Environmental Management, 26; 643-658.
Bogardi, J., L. Duckstein, 1992. Interactive multiobjective analysis embedding the decision maker's implicit preference function. Water Resour Bull, 28; 75-78.
Castelletti, A., R. Soncini-Sessa, 2007. Bayesian networks in water resource modelling and management. Environmental Modelling and Software, 22; 1073-1074.
Cerco, C.F., T. Cole, 1995. User’s guide to the CE-QUAL-ICM three-dimensional eutrophication model, Release version 1.0. Technical report EL-95-15, US army engineer waterways experiment station, Vicksburg, MS.
Dehghani, A. A., M. I. Zanganeh, A. Mosaedi, N. Kohestani, Comparison of suspended load estimation using two methods of sediment gauge curve and artificial neural network (case study of Dogh river in Golestan province). Agricultural sciences and natural resources, 2009. 16(1); 266-276.
Ebrahimi Azarkhoran, F., M. Ghorbani, A. Selajgeh, M. Mohseni Saravi, 2013. Analysis of the social network of local stakeholders in the action plan of cooperative management of water resources. Journal of Watershed Sciences and Engineering of Iran, 8(25); 47-56.
Esfandiari Darabad, F., J. Ebrahim Beheshti, M. H. Fathi, 2014. Landslide susceptibility zoning using Bayesian theory (case study: Siahroud watershed). Geography and Environmental Hazards, 12; 1-10.
Feiz Nia. S., F. Majdabadi Farahani, M. Mohseni Sarvi, M. Arabakhdari, 2002. the length of the statistical period suitable for estimating the average annual sedimentation and its relationship with the area, annual sedimentation changes, climatic, geological and vegetation characteristics of the watershed. Gorgan Journal of Agricultural Sciences and Natural Resources, 9(3); 3-16.
Fisher, D.S., J.L. Steiner, D.M. Endale, J.A. Stuedeman, A.J. Franzluebbers, S.R. Wilkinson, 2000. The relationship of land use practices to surface water quality in the upper Oconee watershed of Georgia. Forest Ecology and Management, 128; 39-48.
Forrester J.W., 1961. Industrial dynamics. MIT Press, Cambridge, 340 p.
Ghaffari, G., M. Mahdavi, 2014. Effect of wet and dry periods on the amount of specific sediment (Case Study: Kharkheh Watershed). Iran-Watershed Management Science and Engineering, 9(30); 49-54.
Garriga, R.G., A. Pérez Foguet, J.L. Molina, J. Bromley, C. Sullivan, 2009. Application of Bayesian networks to assess water poverty, II International Conference on Sustainibility Measurement and Modelling ICSMM 09, Barcelona, 1-24.
General Directorate of Natural Resources and Watershed Management of Semnan Province, 2015. Watershed studies of Damghanroud area.
Johnson, R. E., C.-H. (D.). Chang, L.-Q. Yang, (2010). Commitment and motivation at work: The relevance of employee identity and regulatory focus. The Academy of Management Review, 35(2); 226–245.
Hallding K., 2001. Sustaining Beijing’s Water Supply: A scenario approach to Integrated Water Basin Management, SEI, Water program, 220 p.
Harris, C.K., P.L. Wiberg, 2001. A two-dimensional, time-dependent model of suspended sediment transport and bed reworking for continental shelves. Computers & Geosciences, 27; 675-690.
Hosseinpur, A., M. A. Ghorbani, S. Darbandi, 2014. Estimation of Suspended Sediment Load Using Meta-Heuristic Methods in Ahar Chai River. Geographical, 14 (46); 113-132.
Howes, M. J., M. McKenzie, B. Gleeson, R. Gray, 2010. Adapting Ecological Modernisation to the Australian Context. Journal of Integrative Environmental Sciences, 7(1); 213-227.
Jincheng, L., H. Mengchen, M. Wenjing, L. Yong, F. Feifei, Z. Rui, Ch. Yihui, 2023. Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach. Journal of Environmental Managemen, 4; 232-245.Keshtkar, A.R., A. Salajegheh, A. Sadoddin, M.G. Allan, 2013. Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment). Ecological Modelling, 268; 48-54.Kuikka, S., P.Vari. 1997. The impacts of increased codend mesh size in the Baltic herring fishery: ecosystem and market uncertainty. ICES Journal of Marine Science, 53: 723–730
Li, J., M. Hu, W. Ma, Y. Liu, F. Dong, R. Zo, Y. Chen, 2023. Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach. Journal of Environmental Management, 331; 117280. https://doi.org/10.1016/j.jenvman.2023.117280
McNay, S., K. Zimmerman, R. Ellis, 2005. Caribou habitat assessment and supply estimator (CHASE): using modeling and adaptive management to assist implementation of the Mackenzie LRMP in strategic and operational forestry planning. Internal report. Wildlife Infometrics Inc., Mackenzie, B.C. Wildlife Infometrics Inc, 55.
Mdadgar Sh., H. Moradkhani, 2014. Spatio-temporal drought forecasting within Bayesian networks. Journal of Hydrology, 512, (6); 134-146. https://doi.org/10.1016/j.jhydrol.2014.02.039.
Nyberg, L., J. Eriksson, A. Larsson, P, Marklund, 2006. Learning by doing versus learning by thinking: An fMRI study of motor and mental training. National Library of Medicine, 44(5); 711-723.
Renali, M., H. Deliri, 2010. The effect of social capital on economic growth: explaining the theoretical framework. Scientific journal Economic policy research, 2; 23-56.
Smith, R., E. MacKenzie, S. B. X. Yang, L. M. Buchholz, W. K. Darley, 2007. Modeling the Determinants and Effects of Creativity in Advertising. Journal of Marketing Science, 26(6); 819-833.
Silakhouri, Z., A. Vahabzadeh Kobria, H. R. Pourqasmi, 2023. Preparation of landslide susceptibility map using Bayesian model (case study: part of Talar watershed, Mazandaran province). Environmental Erosion Research, 13(2); 1-16.
Soleimanipour, M., A. Saraf, 2019. Assessing the effects of climate change on the water resources of Lar catchment using SWAT model and comparing its results with Bayesian networks and hybrid intelligent models. Natural Geography, 13(50); 61-79.
Sperotto, A., J.L. Molina, S. Torresan, A. Critto, M. Pulido-Velazquez, A. Marcomini, 2019. A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading. Environmental Science & Policy, 100; 21-36. https://doi.org/10.1016/j.envsci.2019.06.004
Woolcock, M. D. Narayan, 2000. "Social Capital: Implications for Development Theory", The World Bank Research Observer, 15; 225–249. | ||
|
آمار تعداد مشاهده مقاله: 23 تعداد دریافت فایل اصل مقاله: 46 |
||