Leveraging Artificial Intelligence for Environmental Hazard Management in Mountainous Ecosystems

The use of Artificial Intelligence (AI) in managing natural and human hazards in mountainous areas has the potential to reduce damage and enhance resilience. This study, through a review of library-based studies and practical examples, analyzes the application of AI in managing earthquake hazards, slope movements, floods, forest fires, vegetation degradation, and urban development. The general information in this research is based on library studies, but in the practical examples section, satellite images from MODIS, Sentinel-1, and Landsat were used. The main tools used in this study were GMT, TerrSet, Google Earth Engine, and ArcGIS. In order to examine the effects of artificial intelligence on environmental hazard management in mountainous areas, the study investigates the importance of using this technology in addressing earthquake hazards, landslides, floods, vegetation degradation, fires, physical urban development, and water resource pollution. The results of this study have shown that for natural hazards, artificial intelligence, by integrating multi-source data, has been able to provide more accurate predictions compared to traditional methods. This higher accuracy not only increases response time but also enables preventive planning and reduces human and financial losses. For example, flood prediction with LSTM models and early warning of landslides using CNN are practical examples of these capabilities. In the field of human hazards, AI acts as an advanced analytical tool and early warning system. These include continuous monitoring of vegetation changes with the NDVI index, rapid detection of fires with convolutional neural networks, and modeling of pollutant pathways in aquifers, demonstrating the high capabilities of this technology in protecting sensitive mountain ecosystems. Based on the obtained results, this technology, with its capability to process vast amounts of data and identify complex patterns, can serve as an efficient and innovative tool for predicting, monitoring, and mitigating the impacts of these hazards.
 
Extended Abstract
 
Introduction
The mountainous regions of the country, particularly the Alborz and Zagros Mountain ranges, with their complex geological characteristics, steep slopes, diverse vegetation cover, and specific climatic conditions, are prone to incidents that not only cause considerable human and financial losses but also threaten the stability of sensitive ecosystems. Managing these hazards requires precise tools for prediction, monitoring, and decision-making that can analyze diverse environmental, climatic, and human data and provide practical solutions for risk reduction. In recent decades, significant advances in data processing technologies and machine learning algorithms have made artificial intelligence one of the most efficient tools for crisis management. With its ability to process massive volumes of data in short timeframes, extract complex patterns, and predict nonlinear phenomena, artificial intelligence can play an effective role in various stages of risk management from prevention and forecasting to response and recovery in mountainous areas. The input data for these systems may include remote sensing information, ground-based sensor data, hydrological models, meteorological data, and even social and economic data. The application of artificial intelligence in managing natural and human hazards in mountainous areas covers a wide range. Despite these advantages, the full utilization of AI’s potential in managing hazards in Iran’s mountainous regions requires overcoming challenges such as the absence of integrated data infrastructure, limited access to high-quality data, the need for localized algorithms, and the shortage of skilled human resources. Therefore, the present study aims to review and analyze the role of artificial intelligence in managing various natural and human hazards in the country’s mountainous areas, and by identifying capacities, limitations, and successful examples, offer practical solutions to enhance the resilience of these regions.
 
Methodology
This study was designed to develop and evaluate an artificial intelligence framework for predicting and managing natural hazards in mountainous regions. The general information in this research was based on library studies; however, in the practical examples section, satellite images from MODIS, Sentinel-1, and Landsat were used. The main tools employed in this research included GMT (for preparing maps of ground surface changes), TerrSet (for preparing maps of settlement areas and predicting their physical development trends), Google Earth Engine (for preparing maps of flooded areas and vegetation cover), and ArcGIS (for preparing output maps). In this study, to examine the impacts of artificial intelligence in managing environmental hazards in mountainous regions, the importance of using this technology was investigated in relation to earthquake hazards, landslides, floods, vegetation degradation, wildfires, urban physical development, and water resource pollution. It should be noted that for the topics of earthquakes, floods, vegetation degradation, and urban physical development, practical examples were provided.
 
Results and Discussion
The conducted investigations showed that Artificial Intelligence (AI) can play a key role in managing a wide range of natural and human-induced hazards in Iran’s mountainous regions. In the field of earthquakes, the use of deep neural networks and reinforcement learning algorithms, combined with seismological data, GPS, radar imagery (InSAR), and hydrogeochemical data, enables the identification of earthquake precursors and the assessment of crustal stress. For landslides, algorithms such as Random Forest and Convolutional Neural Networks, utilizing spatial and environmental data, generate susceptibility maps and short-term warnings. Regarding floods, LSTM models and satellite rainfall data (GPM and TRMM), along with IoT sensors, provide early prediction and warning capabilities. In managing human-induced hazards, machine learning algorithms and computer vision are effectively used to monitor vegetation cover changes (NDVI), predict wildfires, model water pollution pathways, and analyze urban development. However, challenges such as the lack of integrated data infrastructure, insufficient high-quality data, the need for localized algorithms, and a shortage of specialized personnel are major obstacles to fully leveraging AI capabilities.
Conclusion
The results of this study indicate that integrating AI into the management of natural and human-induced hazards can significantly enhance resilience, reduce damages, and improve decision-making in Iran’s mountainous regions. AI-based approaches, capable of simultaneously analyzing multi-source data, identifying complex patterns, and providing accurate predictions, can fill the gaps in traditional methods. Despite these advantages, realizing the full potential of AI requires overcoming existing barriers. First, establishing a national integrated database, including seismological, meteorological, remote sensing, and spatial information with open access for researchers, is essential. Second, developing localized algorithms compatible with Iran’s specific geological and climatic conditions should be prioritized. Third, investment in training and educating specialized personnel in AI, remote sensing, and GIS can address skill shortages. Additionally, inter-organizational collaboration among research centers, disaster management agencies, universities, and the private sector should be strengthened to use existing capacities in a coordinated manner. Implementing pilot projects in high-risk areas to practically evaluate AI models and deploy real-time data-based early warning systems is also recommended. Finally, the expansion of AI use should align with sustainable development approaches, considering environmental and social aspects, to reduce risks while protecting sensitive mountain ecosystems.