برآورد احتمال رخداد آلودگی نیترات آب زیرزمینی با استفاده از الگوریتم تقویت شیب شدید

Introduction and Goal
Groundwater, as a vital source of fresh water, plays a fundamental role in supplying drinking, agricultural, and industrial needs in many arid and semi-arid regions of the world. However, increased human and industrial activities have led to the exacerbation of pollution in these valuable resources. In this regard, nitrate pollution, due to its high solubility and mobility in water, is recognized as one of the most serious threats to human health and aquatic ecosystems. The consumption of nitrate-contaminated water can lead to various diseases, including methemoglobinemia (blue baby syndrome) in infants and even some cancers in adults. Furthermore, the entry of nitrates into surface waters can result in eutrophication and the degradation of aquatic ecosystems. Given the importance of the issue and the necessity of protecting groundwater resources, this research was conducted with the aim of developing an integrated and comprehensive framework for estimating the probability of groundwater contamination, especially with a focus on nitrate contaminant, in the Lenjanat Plain region located in Isfahan Province, Iran. Using this framework and employing advanced modeling and spatial analysis approaches, areas prone to contamination were identified, which will help in providing effective management solutions to reduce the risks associated with groundwater contamination. The results of this research can serve as a basis for future planning in the sustainable management of water resources and the protection of community health.
Materials and Methods
In this study, data related to the nitrate concentration in groundwater sources were carefully examined. Therefore, crucial information was collected from 102 wells in the Lenjanat Plain of Isfahan Province. Each of these wells represented the nitrate status in the groundwater aquifers of the studied region. To analyze this large volume of data and extract hidden patterns, the Extreme Gradient Boosting model was used. This model was chosen due to its high capability in identifying complex and non-linear relationships between variables, as well as its acceptable prediction precision. In addition to nitrate concentration data, ten key environmental and anthropogenic factors potentially influencing nitrate contamination in groundwater were identified and incorporated into the analytical model. These factors included slope, elevation, drainage density, topographic wetness index, soil order, and distance from streams, lithology, and land-use. By integrating these eight factors into the Extreme Gradient Boosting model, it was possible to identify the most significant factors affecting nitrate contamination and also to spatially predict the probability of nitrate contamination in groundwater.
Results and Discussion
The results of this study clearly demonstrated the effectiveness and efficiency of the Extreme Gradient Boosting in predicting nitrate contamination in groundwater. The overall accuracy of this model was 0.86 which allowed the contamination status of the studied area to be well distinguished. In addition, other performance evaluation criteria of the model also indicated its high accuracy in correctly identifying contaminated and uncontaminated areas; with the area under the ROC curve was equal to 0.85. Moreover, the model recall was found to be 0.80, indicating that 80% of all the real contaminated areas were correctly identified using this model. Finally, the F1-score statistic, which is a combined measure of precision and recall, with a value of 0.83, indicates a good balance between these two measures and the overall reliable performance of the model. The sensitivity analysis of the model revealed that the effect of certain input variables on the spatial estimation of nitrate contamination in groundwater was significant. Among the ten environmental and anthropogenic factors examined, precipitation (21%) and elevation changes (18%) were identified as the most influential and important variables in determining the spatial pattern of nitrate contamination. These findings highlight the importance of natural and geomorphological characteristics of the region in controlling the dispersion and accumulation of nitrates in groundwater and can serve as a useful guide for future studies and the development of targeted management strategies.
Conclusion and Suggestions
One of the important achievements of this study was the production of hazard maps that clearly identified areas with high risk of nitrate contamination in the central part of the studied plain. It is recommended that water resource managers and urban and rural planners use these maps as a valuable tool for taking preventive measures in sensitive areas. Notably, the role of human activities in increasing the risk of nitrate contamination was strongly confirmed by the significant overlap of high-risk areas with agricultural land-use. Based on these findings, it is suggested that nitrogen fertilizers be used optimally for the protection of groundwater resources and the sustainable management of agricultural activities.