شناسایی عوامل مؤثر بر زمین لغزش در جاده آستارا تا تونل نمین با استفاده از مدل MLP (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
زمین لغزش یکی از پدیده هایی است که رخداد آن سبب ایجاد خسارات زیادی به ویژه در مناطق کوهستانی می شود. بنابراین ارزیابی و شناخت عوامل مؤثر بر وقوع رخداد زمین لغزش در مناطق کوهستانی امری ضروری است. هدف از این پژوهش شناسایی عوامل مؤثر بر زمین لغزش در جاده آستارا تا تونل نمین با استفاده از مدل MLP می باشد. مدل MLP یکی از مدل های کارآمد شبکه عصبی است که توانایی حل مسائل پیچیده را دارد. برای شناسایی عوامل مهم در رخداد زمین لغزش با توجه به مطالعات میدانی 8 عامل شناسایی شده است که شامل: زمین شناسی، پوشش گیاهی، فاصله از جاده، کاربری راضی، شیب، جهت شیب، ارتفاع، نقاط واقعی زمین لغزش استفاده شده است. بعد از پیش پردازش ها تمام لایه ها وارد نرم افزارSPSS MODELER شده و مدل سازی با 8 نورون ورودی 6 نورون میانه و 1 خروجی طراحی شده است. نتایج این پژوهش نشان داده که خروجی وزنی مدل MLP بیشترین ارزش وزنی را برای لایه زمین شناسی با مقدار 26/0 برای لایه کاربری اراضی و فاصله از جاده به ترتیب مقدار 14/0 و 13/0 اختصاص داده است. هم چنین در بخش اعتبار سنجی مدل مقدار AUC عدد 948/0 در بخش آموزش و 962/0 در بخش تست شبکه را نمایش می دهد که گویای این است که مدل هم در بخش آموزش و هم در بخش تست دارای اعتبار بالایی می باشد بنابر این نتیجه گیری می شود که عامل زمین شناسی نسبت به سایر عوامل در رخداد زمین لغزش در منطقه تأثیر زیادی دارد و درنهایت پیشنهاد می گردد در مطالعات آتی برای مطالعه و ارزیابی زمین لغزش و حرکات دامنه ای از مدل های یادگیری ماشینی و هوش مصنوعی استفاده شودIdentifying Factors Affecting Landslides on Astara Road to Namin Tunnel Using the MLP Model
Landslides are one of the phenomena that cause significant damage, especially in mountainous areas. Therefore, it is essential to evaluate and identify the factors affecting the occurrence of landslides in these areas. The purpose of this research is to identify the factors affecting landslides along the Astara road to Nemin tunnel using an MLP model. The MLP model is one of the efficient neural network models that can solve complex problems. To identify the important factors in the occurrence of landslides, based on field studies, 8 factors have been identified, including: geology, vegetation, distance from the road, land use, slope, aspect, elevation, and recorded landslide locations. After pre-processing, all layers were entered into SPSS MODELER software, and the modeling was designed with 8 input neurons, 6 hidden layer neurons, and 1 output neuron. The results of this research showed that the weighted output of the MLP model assigned the highest weight to the geological layer (0.26), followed by the land use layer and distance from the road (0.14 and 0.13, respectively). For model validation, the AUC value was 0.948 for the training data and 0.962 for the testing data, indicating the model's high reliability in both phases. Therefore, it can be concluded that the geological factor has the greatest impact on the occurrence of landslides in the region compared to other factors. Finally, machine learning models and artificial intelligence are recommended for future studies on landslides and mass movements. Extended Abstract Introduction Geomorphic phenomena are natural occurrences that manifest with specific spatial and temporal characteristics. According to the Geological Society, a landslide is defined as the downward movement of mass materials on sloping surfaces. Landslides cause significant damage worldwide each year and are recognized as one of the most critical geomorphological hazards. Natural factors such as earthquakes, rainfall, and snowmelt, along with human activities such as road construction, infrastructure development, and mining, exacerbate landslide occurrences, particularly in mountainous regions. One key strategy to mitigate damages caused by mass movements, particularly landslides, is the accurate and timely identification of areas with instability potential. Determining the factors influencing landslides and categorizing associated risks are crucial for developing effective mitigation strategies. The Astara–Namin tunnel road, a major route connecting Gilan and Ardabil provinces, is highly susceptible to landslides due to its unique geomorphic and geological conditions, its location in a mountainous region, and its specific climatic characteristics. Given the importance of this road and the associated slope hazards, especially landslides, conducting geomorphological studies to identify influencing factors is imperative. These studies can lead to effective planning and measures to reduce risks, prevent human and financial losses, and protect the local environment. This research aims to identify the factors affecting landslides along the Astara–Namin tunnel road using the Multilayer Perceptron (MLP) model. Material and Methods This study utilized various geospatial datasets, including a 1:100,000-scale geological map of Astara County for geological formations, a 1:50,000-scale topographic map for road layers, and a 12.5-meter resolution DEM from ALOS-PALSAR. Sentinel-2 satellite imagery was employed for land use and vegetation cover analysis, while GPS data was used to map landslide-prone areas. The Multilayer Perceptron (MLP) model is a type of artificial neural network inspired by the structure and functionality of the human brain. The MLP model is highly effective in solving complex problems by recognizing patterns and relationships in data. Results and Discussion The results indicate that the geological layer holds the highest weight (0.26), followed by land use (0.14) and distance from the road (0.13), suggesting that landslide occurrences along the Astara–Namin tunnel road are primarily influenced by these factors. The study classifies landslide-prone areas into four risk levels—low, medium, high, and very high—based on the Jenks natural breaks classification method. The area distribution for each risk class in the MLP model is as follows: very high-risk zones (23.14 km²), high-risk zones (47.2 km²), medium-risk zones (21.19 km²), and low-risk zones (0.9 km²). These findings demonstrate the model's capability in identifying critical landslide-prone areas along the studied route. Conclusion The presence of andesite and sedimentary rock layers, such as limestone and other fragile formations, contributes to slope instability under external dynamic forces. Excessive water intake from rainfall and snowmelt saturates the surface layers of slopes, transforming the soil into a semi-fluid state. Since the lower layers are more resistant, the upper layers tend to move downslope, leading to landslides. Additionally, human activities significantly influence landslide occurrences in the region. Extensive land use changes, particularly those that disturb natural vegetation and soil stability, exacerbate landslide risks. The MLP algorithm evaluation demonstrated high reliability in both training and testing phases, making it a valuable tool for identifying landslide-influencing factors. The distinguishing feature of this algorithm is its design, which mimics the human brain's neural networks, enabling it to solve complex problems effectively. This study highlights the necessity of incorporating advanced computational models such as MLP in geomorphological hazard assessments to enhance predictive accuracy and risk mitigation strategies.
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