شناسایی عوامل موثر و پهنه‌بندی خطر زمین‌لغزش با استفاده از روش حداکثر آنتروپی (مطالعه موردی: حوزه آبخیز چالوس)

زمین‌لغزش‌ها یکی از مخرب‌ترین نوع حرکات و ناپایداری‌های دامنه‌ای هستند که همواره موجب فرسایش خاک، تولید رسوب، از بین بردن زمین‌های زراعی، باغی و جاده‌ها می‌شوند. همچنین باعث خسارات جانی و مالی قابل توجهی در نقاط مختلف جهان به ویژه در کشور ایران به دلیل دارا بودن شرایط خاص ساختار زمین‌شناسی و زمین‌ریخت‌شناسی می‌شود؛ به همین منظور در این پژوهش به پهنه‌بندی خطر زمین‌لغزش با استفاده از روش حداکثر آنتروپی در حوزه آبخیز چالوس استان مازندران پرداخته شد. جهت شناسایی مهم‌ترین متغیرهای تاثیرگذار و تعیین اهمیت نسبی هریک از عوامل موثر بر شناسایی مناطق زمین‌لغزش و تحلیل حساسیت مدل، از الگوریتم حداکثر آنتروپی به کمک نرم‌افزارmaxent استفاده شد؛ سپس به منظور ارزیابی مدل از منحنی roc استفاده شد و سطح زیر نمودار auc بدست آمده به عنوان معیاری از قدرت تفکیک مدل در تشخیص نقاط حضور و عدم حضور مورد توجه قرار گرفت؛ در نهایت بر اساس عوامل موثر، با استفاده از نرم‌افزار arc gis10.8 نقشه پهنه‎بندی خطر وقوع زمین لغزش در حوزه آبخیز چالوس تهیه شد. بر اساس نتایج حاصل از مدل، عامل‌های بارندگی، خاک‌شناسی، واحد‌های زمین‌شناسی، درصد شیب، کاربری اراضی و فاصله از رودخانه به ترتیب موثرترین عوامل در بروز زمین لغزش در منطقه مورد مطالعه معرفی شدند. مقدار auc برای اعتبارسنجی پهنه لغزشی 0.73 به دست آمد که نشان‌‌دهنده شناسایی و مدلسازی قابل قبول زمین لغزش توسط مدل در منطقه مورد مطالعه بود. طبق نتایج به دست آمده از پژوهش حاضر طبقات خطر کم، نسبتاً کم، متوسط، نسبتاً زیاد و زیاد به ترتیب 13.29، 18.57، 23.73، 35.90 و 8.49 درصد از محدوده مورد مطالعه را به خود اختصاص دادند که این مهم نشان دهنده پتانسیل بالای منطقه جهت ایجاد زمین لغزش است؛ لذا با توجه به نتایج حاصل از این پژوهش، براساس عوامل موثر شناسایی شده، در مناطق دارای اولویت با راهکاری مناسب و تحلیل مسائل مدیریتی می‌توان جهت ارتقای سطح مدیریت حوزه‌های آبخیز و حفاظت خاک و آب برنامه‌ریزی نموده و خسارات ناشی از زمین لغزش را کاهش داد.

identifying effective factors and landslide risk zoning using the maximum entropy method (case study: chalus watershed)

introduction landslides are one of the most devastating natural hazards, occurring suddenly and causing widespread destruction to the environment and human communities. these phenomena result from the movement of large masses of soil and rock on steep slopes, triggered by various factors such as heavy rainfall, earthquakes, climate change, and human activities. landslides can lead to the destruction of infrastructure, homes, roads, and agricultural lands, affecting thousands of lives. globally, landslides are a major concern, particularly in mountainous and hilly regions. they not only cause direct damage to structures and infrastructure but also lead to soil erosion, loss of natural habitats, and economic disruption. rapid population growth and urban development further exacerbate the risk and impact of landslides. therefore, understanding the factors that contribute to landslide occurrences and employing advanced prediction and management techniques are crucial. modern tools and models, such as geographic information systems (gis) and mathematical models, play a vital role in identifying landslide-prone areas and developing effective mitigation strategies. collaborative efforts among researchers, engineers, and policymakers are essential to enhance community resilience against landslides. by integrating advanced technologies and continuous monitoring, we can better predict and manage the risks associated with landslides, ultimately safeguarding lives and promoting sustainable development in vulnerable regions. this concerted approach will not only minimize the immediate impacts of landslides but also ensure long-term stability and prosperity for affected communities. based on this, the current research was planned with the aim of landslide risk zoning using the maximum entropy method in the chalus watershed of mazandaran province.materials and methodsto identify the most influential variables and determine the relative importance of each factor in recognizing landslide-prone areas and performing sensitivity analysis of the model, the maximum entropy algorithm was employed. after developing a comprehensive model incorporating all variables, the modeling process was repeated by excluding each variable one at a time. this approach allowed for assessing the impact of each variable on predicting susceptible areas. subsequently, the receiver operating characteristic (roc) curve was used to evaluate the model, and the area under the curve (auc) obtained was considered as a measure of the model's discriminative power in identifying presence and absence points. to prepare the stability index, the sinmap extension and arcgis 10.8 software were utilized. by accepting default values for calibrating the variables and applying the relevant settings and values, the stability index was derived. in the final stage, based on the influential factors, a landslide hazard zoning map for the chalus watershed was prepared using the arcgis 10.8 software.results and discussionbased on the model results, the factors of rainfall, soil science, geological units, slope percentage, land use, and distance from the river were identified as the most influential in the occurrence of landslides in the study area. the auc value for validating the landslide zoning was obtained as 0.73, indicating the model’s acceptable identification and modeling of landslides by the model in the study area. according to the results, the low, relatively low, moderate, relatively high-, and high-risk classes respectively occupied 13.29%, 18.57%, 23.73%, 35.90%, and 8.49% of the study area, indicating the high potential of the region for landslide occurrences. overall, it can be stated that in areas with low resistance formations, suitable slopes for landslide occurrence, and susceptible landforms, the risk of landslides is higher. given that the role of each factor depends on other influential factors, their role in the occurrence or non-occurrence of landslides is not uniform. the combination of these factors creates optimal conditions for the occurrence of this natural phenomenon.conclusionin conclusion, landslide hazard zoning is recognized as one of the essential tools in disaster management and urban and environmental planning. utilizing advanced modeling methods such as maximum entropy and gis-based spatial analysis techniques allow for the accurate identification and mapping of landslide-prone areas. the findings of this research not only clarify the impact of environmental, geological, and human factors on landslide occurrences but also help in providing effective management solutions to reduce risks and enhance community resilience. especially in regions facing landslide hazards, these results can serve as a basis for formulating policies and strategies for prevention and risk reduction. therefore, based on the results of the current research, according to the effective factors in priority areas, with a suitable solution and proper management analysis, it is possible to plan to improve the level of watershed management and soil and water protection, and reduce the damage caused by landslides.