Evaluation of the efficiency of three data mining models in zoning areas prone to gully erosion (Case study: Upper Watershed of Boustan Dam)

Gully erosion is a particularly destructive form of water erosion that can lead to alarming rates of soil loss, especially in the vulnerable landscapes of dry and semi-arid regions. This type of erosion is recognized not only for its immediate impact on land but also as a critical environmental challenge that requires our urgent attention. As a result, there has been a growing emphasis on developing effective predictive models that can elucidate the temporal and spatial dynamics of gully erosion-specifically, how it forms, expands, and evolves over time. This endeavor has captured the interest of soil conservation experts and researchers alike, who understand the profound implications of this issueIn recent years, remote sensing and data mining techniques have emerged as valuable tools for identifying and mapping areas susceptible to gully erosion. These innovative methods provide essential insights for land managers and policymakers, enabling them to make informed decisions. Furthermore, the effectiveness of predictive models hinges on their advanced capabilities, which enhance their learning potential and improve the identification of relationships among various factors. Creating a sensitivity map is an essential strategy for land use planning, as it actively contributes to reducing land degradation and safeguarding our natural resources. Understanding the connection between gully occurrences and influential factors is not only beneficial; it is crucial for sustainable land management and environmental preservation.

This research investigates the sensitivity of the upper basin of the Boustan Dam to gully erosion using object-based techniques and data mining algorithms. To achieve this, field visits were conducted to select 81 gullies for analysis. The study examines several factors, including slope, aspect, slope length index (LS), elevation, plan curvature, distance from the river, drainage density, topographic wetness index (TWI), height above the nearest drainage (HAND), average annual rainfall, distance from roads, distance from faults, land use, geomorphology, soil texture, and satellite bands B7, B5, and B3. Additionally, the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference water index (NDWI) are considered, along with geological aspects. QuickBird satellite images from 2021 and Orfeo software were utilized to monitor and identify gullies in the area through image segmentation. Initially, a collinearity analysis of 23 effective erosion occurrence indices was performed, resulting in the removal of distance from the fault, digital elevation model (DEM), NDWI, NDBI, and satellite bands B3, B5, and B7 due to their collinearity exceeding five. Following this linear operation, all remaining indices were integrated with the segmentation map obtained from the Orfeo environment. Finally, three models -Random Forest, Maximum Entropy, and Support Vector Machine- were employed to model the selected indices using Python (Colab).

The results from the object-oriented method in the Orfeo software further demonstrated its effectiveness in accurately identifying gullies. With an impressive accuracy rate of 91.3%, this method has proven to be highly reliable in generating machine learning maps with high precision. Findings indicate that the key factors contributing to gully erosion include the rainfall index, distance from the river, Height Above Nearest Drainage (HAND) index, distance from the road, and valley index. Torrential rain emerged as a significant driver of gully erosion, while the distance from the river was crucial due to the concentration of surface and subsurface flows toward waterways. The HAND index played a prominent role in modeling the sensitivity of the study area compared to other sub-indices derived from DEM, as it exhibited promising applications in assessing natural hazards. Locations close to roads were found to be more vulnerable to water erosion, and valleys were identified as especially susceptible to gully erosion due to their conducive conditions for rapid water flow and erosion. Extensive field studies support this observation. Furthermore, zoning results generated using these indices indicated that, within the random forest model, 544.23 hectares of the area are at high or very high risk of erosion. This model outperformed the Maximum Entropy and Support Vector Machine models in predicting erosion-prone areas. Finally, the ROC curve was utilized to validate the model, yielding AUC values of 0.95 and 0.94 in the random forest model during the training and validation stages, respectively. These results indicate the model's high accuracy in predicting areas highly susceptible to gully erosion.

This study effectively used object-based image analysis algorithms and data mining techniques to create a sensitivity map of the region. The object-based method efficiently identified the local gullies using the mean shift algorithm, while the random forest algorithm excelled in predicting areas prone to gully erosion. Key factors contributing to gully erosion were identified, including rainfall, distance from the river, soil HAND index, and distance from roads and valleys. The findings from this study provide valuable insights for managing and preserving basin resources. Implementing the recommendations from this research could help mitigate the impacts of gully erosion in the future and ensure the sustainability of the Boustan Dam and its surrounding ecosystem.