پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,230 |
| تعداد مقالات | 67,766 |
| تعداد مشاهده مقاله | 115,221,324 |
| تعداد دریافت فایل اصل مقاله | 89,968,534 |
ارزیابی معیارهای مؤثر بر خطر سیل‌خیزی مبتنی بر فرآیند تحلیل شبکه‌ای و GIS در حوضۀ وازرود استان مازندران | ||
| اکوهیدرولوژی | ||
| مقاله 5، دوره 10، شماره 1، فروردین 1402، صفحه 61-75 اصل مقاله (1.52 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2023.353903.1708 | ||
| نویسندگان | ||
| کریم سلیمانی* 1؛ بهروز محسنی2 | ||
| 1استاد مرکز سنجش از دور و گروه آبخیزداری، دانشگاه کشاورزی و منابع طبیعی، ساری، ایران | ||
| 2استادیار گروه مهندسی و فناوری کشاورزی، دانشگاه پیام نور، تهران، ایران | ||
| چکیده | ||
| پهنهبندی خطر سیل به عنوان مشکلی اساسی همواره مورد توجه بسیاری از محققان است. با توجه به نیاز آن به تحلیل مکانی پیچیده از اینرو، معیارهای متعددی باید ارزیابی شوند. این تحقیق با هدف تهیۀ نقشۀ پهنهبندی خطر وقوع سیل در حوضۀ وازرود واقع در استان مازندران انجام گرفته است. به اینمنظور 6 متغیر شیب، کاربری اراضی، گروه هیدرولوژیک، شمارۀ منحنی، پایداری و بارندگی مورد ارزیابی قرار گرفتند. برای وزندهی معیارها و تهیۀ لایههای اطلاعاتی بهترتیب از فرایند تحلیل شبکه‌ای (ANP) و GIS بهره گرفته شد. نتایج به‌دست‌آمده از تحقیق نشان داد معیار شماره منحنی و شیب بهترتیب با مقدار وزنی 42/1 و 00/1 در اولویت اول و دوم اهمیت خطر وقوع سیل هستند. درنهایت، با تلفیق هریک از لایهها و براساس وزن در محیط GIS، نقشۀ نهایی پهنهبندی سیل به دست آمد. نتایج نشان داد 7/78 کیلومترمربع (36/57 درصد) از مساحت منطقه در معرض خطر وقوع سیل (خیلیزیاد و زیاد) قرار دارد. این امر با وجود نفوذپذیری متوسط، بهدلیل دخالت عواملی مانند شمارۀ منحنی بالا، ارتفاع رواناب بیشتر و پوشش مرتعی ضعیف است که با توجه به ارتفاع زیاد و خوشنشینی احتمالی منطقه طی سالهای آتی، مدیران و برنامهریزان منطقه باید با تمهیدات لازم (کنترل ساخت‌وساز در این مناطق و استفاده از طرحهای کاربری اراضی و ایجاد پوشش گیاهی)، از خطر وقوع سیل در این مناطق جلوگیری کنند یا آن را کاهش دهند. | ||
| کلیدواژه‌ها | ||
| گروه هیدرولوژیک؛ شمارۀ منحنی؛ رواناب؛ فرایند تحلیل شبکه (ANP) | ||
| عنوان مقاله [English] | ||
| Evaluation of effective criteria on flood risk based on network analysis process and GIS in Vazroud basin of Mazandaran province | ||
| نویسندگان [English] | ||
| Karim Solaimani1؛ Behrooz Mohseni2 | ||
| 1Professor, Remote Sensing Centre and Dept. of Watershed Management, Sari Agricultural Sciences and Natural Resources University, PoBox 578, Sari, Iran | ||
| 2Assistant Professor, Department of Engineering and Agricultural Technology, Payam-e- Noor University (PNU), Tehran, Iran | ||
| چکیده [English] | ||
| Flood risk zoning as a fundamental problem is always the concern of many researchers. Due to its need for detailed spatial analysis, therefore, several criteria should be evaluated. This research has been conducted with the aim of preparing a flood risk zoning map in the Vazroud watershed located in Mazandaran province. For this purpose, 6 variables were evaluated slope, land use, hydrological group, curve number, stability, and rainfall. Network analysis process (ANP) and GIS were used to weigh the criteria and prepare information layers, respectively. The results obtained from the research showed that the criteria of curve number and slope with a weighted value of 1.42 and 1.00 are the first and second priorities of flood risk. Finally, the final flood zoning map was obtained by merging each of the layers and based on the weight in the GIS environment. The results showed that 78.7 km2 (57.36 %) of the area is at risk of flooding (high and very high). Despite the medium permeability of the soil, this is due to the involvement of factors such as high curve number, higher runoff depth, and poor vegetation cover, which due to the high altitude and possible prosperity of the region in the future, planners should take necessary procedures (construction control and construction in these areas and the use of land use plans and planting vegetation), prevent or reduce the risk of flooding in these areas. | ||
| کلیدواژه‌ها [English] | ||
| Hydrologic Group, Curve Number, Runoff, Analytical Network Process (ANP) | ||
| مراجع | ||
|
[1]. WHO (World Health Organization). Floods. 2017. Available online: https://www.who.int/health-topics/floods (accessed on 13 January 2022). [2]. UNISDR (United Nations Office for Disaster Risk Reduction). Economic 1998-2017 Losses, Poverty & DISASTERS. 2017; 1-30. Available online: www.unisdr.org (accessed on 21 January 2022). [3]. Ghazanfarpor H, Sedaghat- Kish M, Soleimani Damaneh M, Sabahi-Goraghani Y. On the Evaluation of the Reaction of Urban Managers Facing Flood as an Environmental Hazard with Emphasis on Resiliency (Case Study: Jiroft City). Geography and Sustainability of Environment. 2019; 30: 107-127. [Persian]. [4]. Loan TKH, Umitsu M. Micro-landform classification and flood hazard assessment of the Thu Bon alluvial plain, central Vietnam via an integrated method utilizing remotely sensed data. Applied Geography. 2011; 31: 1082–1093. [5]. Uddin K, Gurung D.R, Giriraj A, Shrestha B. Application of remote sensing and GIS for flood hazard management: a case study from Sindh Province, Pakistan. American Journal of Geographic Information System. 2012; 2 (1): 1–5. [6]. Gholami V, Asghari A, Salimi E.T. Flood hazard zoning using geographic information system (GIS) an HEC-RAS model. Caspian Journal of Environmental Sciences. 2016; 14 (3): 263–272. [7]. Shahiriparsa A, Heydare M, Sadeghian M.S, Moharrampour M. Flood zoning simulation by HEC-RAS Model (Case Study: Johor River-Kota Tinggi Region). Journal of river engineering. 2013; x1 (1): 33–38. [8]. Marchesini I, Rossi M, Salvati P, Donnini M, Sterlacchini D, Guzzetti F. Delineating flood prone areas using a statistical approach, PeerJ Preprints. 2016; 4: e1937v2. [9]. Tehrany M.S, Pradhan B, Jebur M.N. Spatial prediction of flood susceptible areas using rule based, decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology. 2013; 504: 69–70. [10]. Generino PS, Sony E.V, Proceso L.F. Analytic hierarchy process (AHP) in spatial modeling for floodplain risk assessment. International Journal of Machine Learning Computer. 2014; 4 (5): 450–457. https://doi.org/10.7763/IJMLC.2014.V4.453. [11]. Tang Zh, Zhang H, Yi Sh, Xiao Y. Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. Journal of Hydrology. 2018; 558: 144-158. [12]. Gigovi´c L, Pamuˇcar D, Baji´c Z, Drobnjak S. Application of GIS-Interval Rough AHP Methodology for Flood Hazard Mapping in Urban Areas. Water. 2017; 9 (6): pp. 1-26. https://doi.org/10.3390/w9060360. [13]. Singha C, Swain K.C, Meliho M, Abdo H.G, Almohamad H, Al-Mutiry M.Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India. Remote Sensing. 2022; 14 (24): 6229. https://doi.org/10.3390/rs14246229. [14]. Jalaliyan S.I. Evaluating and zoning flooding on a temporal and spatial scale (Study Area: Gorgan River Watershed in Golestan Province). Geographical Planning of Space Quarterly Journal. 2022; 11 (42): 143-162. [Persian]. [15]. Soleimani K. Final report of flood risk zoning project of Mazandaran province (case study: Vazroud basin). Sari Agricultural Sciences and Natural Resources University. 2019; 242. [Persian]. [16]. Alipour A, Mahdavi M. Analyzing the role of local society's environmental understanding played in natural resources operation in the watersheds of the North of Iran. (case study: Vazrood watershed– Noor). Quarterly Geographical Journal of Territory. 2008; 5 (17): 13-26. [Persian]. [17]. Pack R.T. Tarboton D.G, Goodwin C.N. Terrain stability mapping with SINMAP, technical description and users guide for version 1.00, 4114–0, Terratech Consulting Ltd, Salmon Arm. British Columbia.
[18]. Hammond C, Hall D, Miller S, Swetik, P. Level I stability analysis (LISA) documentation for version 2.0. General technical report INT- 285. 1992; p. 36. [19]. Ghorbanzadeh O, Feizizadeh B, Blaschke T. Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses. Geomatics Natural Hazards Risk. 2018b; 9 (1): 127–151. [20]. Mitroulis D, Kitsios F. MCDA for assessing the impact of digital transformation on hotel performance in Thessaloniki. Proceedings of the 8th International Symposium & 30th National Conference on Operational Research; Patras, Greece. 2019; 53–57. [21]. Yariyan P, Karami M.R., Abbaspour R.A. Exploitation of MCDA to Learn the Radial Base Neural Network (RBFNN) aim physical and social vulnerability analysis versus the earthquake (case study: Sanandaj City, Iran). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Volume XLII-4/W18 1071–1078. GeoSpatial Conference – Joint Conferences of SMPR and GI Research 12–14 October, Karaj, Iran. 2019; 8 pp. [22]. Alilou H, Rahmati O, Singh VP, Choubin B, Pradhan B, Keesstra S. Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. Journal of Environment Management. 2019; 232: 22–36. [23]. Peng K.H, Tzeng G.H. Exploring heritage tourism performance improvement for making sustainable development strategies using the hybrid-modified MADM model. Current Issues in Tourism. 2019; 22 (8): 921–947. [24]. Saaty T.L. Decision making—the analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering. 2004; 13 (1): 1–35. [25]. Alizadeh M, Ngah I, Hashim M, Pradhan B, Pour A.B. A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sensing. 2018; 10 (6): 1-34. doi:10.3390/rs10060975. [26]. Khayrizadeh M, Maleki J, Amunia H. Potential flood hazard zoning in Mardeghai catchment using model ANP. Quantitative Geomorphology Research. 2018; 1 (3): 39-56. [Persian]. [27]. Pradhan B. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology. 2010; 9 (2): 9–18. [28]. Zhan X, Huang M. L. Arc CN-Runoff: an ArcGIS Tool for Generating Curve Number and Runoff Maps. Environmental Modelling & Software. 2004; 19 (10): 875–879. [29]. Ouma Y.O, Tateishi R. Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water. 2014; 6: 1515–1545. [30]. Daneshparvar B, Rasi Nezami S, Feizi A, Aghlmand R. Comparison of results of flood hazard zoning using AHP and ANP methods in GIS environment: A case study in Ardabil province, Iran. Journal of Applied Research in Water and Wastewater. 2021; 9 (1): 1-7. [31]. Khalil R. Flood Risk Code Mapping Using Multi Criteria Assessment. Journal of Geographic Information. 2018; 10: 686-698. doi: 10.4236/jgis.2018.106035. [32]. Zoratipour A , Cheraghi M. Combined Application of Multi-Criteria Decision Making Methods and Remote Sensing Systems for Flood Cellular Zoning of Abolabbas River Basin in Khuzestan Province. Irrigation Sciences and Engineering (JISE). 2022; 44 (4): 109-122. [Persian]. [33]. Mokhtari D, Rezaei Moghaddam M.H, Rahimpour T, Moazzez S. Preparing the Risk Map of Flood Occurrence in the Ghomnab Chai Basin Using ANP Model and GIS Technique. Journal of Echo Hydrology. 2020; 7 (2): 497-509. [Persian]. [34]. Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly H-B, Gr_of G, Ho HL, et al. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology. 2019; 573: 311–323. | ||
|
آمار تعداد مشاهده مقاله: 139 تعداد دریافت فایل اصل مقاله: 123 |
||