Identifying the primary drivers of soil erosion through machine learning: A comparative analysis of three algorithms

This research endeavors to unravel the intricacies of soil erosion in Varanasi by employing three machine learning techniques. Varanasi, with its diverse topography, land-use patterns, and climatic conditions, serves as an ideal study area for assessing the multifactorial nature of soil erosion. The study aims to identify the key contributors among various variables, including soil characteristics, topographic attributes, land cover, and climate parameters.

Traditional methods often fail to capture the complex interactions leading to soil erosion, but machine learning offers a more nuanced approach. By simultaneously considering multiple variables and their interrelationships, this technique provides a comprehensive understanding of the dominant causes of soil erosion.

The literature review encompasses a comprehensive exploration of soil erosion prediction and assessment methodologies, drawing insights from key studies conducted across various regions. Nearing et al.’s (2005) foundational work emphasized the integration of the Revised Universal Soil Loss Equation (RUSLE) and geo-information technology for predicting soil erosion in large river basins, highlighting the importance of spatial information in erosion risk assessment. Subsequent studies, such as Ndiaye et al. (2017), compare empirical models for estimating soil erosion and sediment yield in Africa, contributing to the understanding of diverse modeling approaches under varying environmental conditions. Panagos et al. (2018) advanced the field by predicting soil erosion risk through the integration of GIS-based RUSLE, remote sensing, and geostatistical techniques, emphasizing the need for a holistic approach. The significance of region-specific analyses is underscored by Dikinya et al. (2019), who assess soil erosion vulnerability in the Upper Blue Nile River basin, Ethiopia, recognizing the unique characteristics of the study area. Bosco et al. (2018) further contributed to the literature by modeling soil erosion at the European scale, emphasizing harmonization and reproducibility for consistent assessments across diverse regions. Abate et al.’s (2017) study explored soil erosion risk in the Upper Blue Nile River basin, Ethiopia, using RUSLE and geo-information technology, showcasing the applicability of integrated methodologies in regions with varying topography and land use. The subsequent studies, spanning regions from Iran and India to Malaysia and China, employ RUSLE and GIS tools for soil erosion prediction, providing region-specific insights into erosion dynamics. Collectively, these works offer a rich foundation for understanding soil erosion causes and processes, laying the groundwork for the current research focused on Varanasi, India, using multivariate regression to identify the dominant factors influencing soil erosion in this unique agroecological context.

Liu et al. (2024) provided a comprehensive review of various machine learning methods applied to soil erosion risk, highlighting the strengths and limitations of these approaches in different contexts. Zhang et al. (2024) demonstrated the effectiveness of deep learning models for soil erosion prediction in the Loess Plateau, illustrating the potential of advanced neural networks to improve accuracy over traditional methods. Sharma et al. (2023) explored the integration of remote sensing data with machine learning techniques, emphasizing how these combined approaches offer improved assessment capabilities. Mirzaee et al. (2018) investigated the integration of ensemble learning techniques with traditional soil erosion models, revealing enhanced prediction accuracy through this hybrid approach. Similarly, McInerney et al. (2023) conducted a comparative study of machine learning algorithms for predicting soil erosion and sediment yield, underscoring the superior performance of certain algorithms in diverse settings. Collectively, these studies highlight the transformative impact of machine learning on soil erosion modeling and underscore the need for continued innovation and refinement in this field.

In recent years, machine learning techniques have been extensively applied to predict suspended sediment concentration (SSC) in rivers, addressing challenges in hydrological modeling with data-driven approaches. Rahul et al. (2021) and Rezaei et al. (2021) utilized artificial neural networks and support vector machines to model daily SSC, achieving promising results in predictive accuracy. Similarly, Achite et al. (2022) and Hanoon et al. (2022) conducted comparative analyses of advanced machine learning algorithms, confirming the efficacy of ensemble models for SSC prediction. Sharafati et al. (2020) explored ensemble machine learning models, highlighting uncertainty analysis as a critical component in SSC forecasting. In related work, Aires et al. (2023) focused on sediment concentration modeling in the Doce River basin, using diverse machine learning approaches for enhanced prediction in complex river systems. Additionally, Khatri et al. (2023) investigated climate change forecasting through data mining techniques, emphasizing the relevance of robust machine learning models for hydrological applications.

The significance of this research lies in its potential to enhance our understanding and management of soil erosion, a critical environmental issue impacting agriculture, water quality, and ecosystem health worldwide. By leveraging machine learning to identify the primary drivers of soil erosion, this study offers a data-driven approach to isolating the most influential factors contributing to soil degradation. The comparative analysis of different algorithms provides a clearer picture of the models best suited for accurately predicting erosion risks, aiding decision-makers in choosing effective and scalable tools for erosion monitoring and control. Furthermore, this research can inform targeted soil conservation practices, support sustainable land-use planning, and ultimately contribute to mitigating the adverse effects of erosion on both local and global scales. In doing so, this research strives to pave the way for sustainable land management practices and contribute to the broader discourse on mitigating soil erosion impacts.

The main objectives of this research are:

• (1) to identify the dominant factors influencing soil erosion in the Varanasi region using advanced statistical methods, particularly multivariate regression, AdaBoost regression, and gradient regression. The study aims to unravel the intricate interactions among key variables, including soil properties, topography, land cover, and climate factors, within the unique agroecological context of Varanasi;

• (2) to compare all three algorithms and find the best one for this study; and

• (3) to propose deterrence techniques for the study area to reduce soil erosion.

The soil erosion data utilized in this study encompasses information from a total of 46 watersheds, obtained from the Indian Institute of Technology (IIT) Banaras Hindu University (BHU). This dataset was meticulously estimated through the application of remote sensing techniques and the analysis of satellite imagery by the researchers at the institute. The remote sensing data and satellite images were procured from the USGS Earth Explorer website. The dataset includes 16 distinct parameters that significantly influence soil erosion and sediment yield across all 46 watersheds. For a comprehensive view of the parameters and their respective values, please refer to Table 1. It contains 16 parameters like soil type, slope, land use and land cover, and runoff. Sediment yield depends on all these 16 parameters. Details of soil classes (Soil 1, Soil 2, Soil 3, Soil 4, and Soil 5) are given in Table 2.

To develop and validate our predictive models, we employed a standard data partitioning approach. Specifically, 80% of the entire dataset was allocated for training purposes, which facilitated the calibration of our models. The remaining 20% was set aside for validation, allowing us to assess the accuracy and robustness of the models.

The focal point of this research is the region encompassing Varanasi and its adjacent areas in Uttar Pradesh (UP), India (Figure 1). Varanasi, a historically and culturally significant city, serves as the epicenter of this study, extending its scope to the surrounding landscapes of UP. This region, characterized by diverse topography, land-use patterns, and soil types, presents an intriguing setting for investigating soil erosion dynamics. The intricate interplay of factors such as rainfall intensity, slope gradient, land-use practices, soil composition, and vegetation cover contributes to the complexity of erosion processes in this locale. Through meticulously examining these elements, this research aims to unravel the principal contributors to soil erosion, employing multivariate regression modeling to discern patterns and relationships within this dynamic environmental context.

Multivariate regression is a statistical technique that extends simple linear regression by considering multiple independent variables to predict a dependent variable. In the context of soil erosion research, multivariate regression modeling involves analyzing the relationships among various factors that contribute to soil erosion (Zhang & Bai 2017). The technique allows researchers to assess the combined influence of multiple predictor variables on the outcome, such as soil erosion and runoff.

Key components:

• (1) Dependent variable: In this context, the dependent variable could be soil erosion or runoff, representing the phenomena under investigation.

• (2) Independent variables (predictors): Factors influencing soil erosion, such as rainfall intensity, slope gradient, land use, soil type, and vegetation cover, serve as independent variables.

• (3) Equation formulation: The multivariate regression equation is expressed as follows:

  

AdaBoost, short for Adaptive Boosting, is a machine learning algorithm that belongs to the ensemble learning family. It works by combining multiple weak learners, often decision trees or stumps (small decision trees with only one split), to create a strong predictive model.

Here is how AdaBoost works (Figure 2):

• Sequential training: AdaBoost trains a series of weak learners sequentially. In each iteration, the algorithm pays more attention to the instances that were misclassified by the previous weak learners. This sequential training process helps the model focus on difficult-to-classify instances, gradually improving its overall performance.

• Weighted voting: After training each weak learner, AdaBoost assigns a weight to it based on its performance. Weak learners with higher accuracy are given higher weights, indicating their importance in the final ensemble model. During prediction, the weak learners' outputs are combined through a weighted majority vote, where the weights assigned to each learner determine their influence on the final prediction.

• Model aggregation: The final prediction of the AdaBoost model is a weighted sum of the predictions made by all weak learners. By aggregating the predictions of multiple weak learners, AdaBoost can effectively capture complex patterns and relationships in the data, leading to a robust predictive model.

AdaBoost is particularly effective in handling classification tasks, but it can also be adapted for regression problems, known as AdaBoost regression. In regression, AdaBoost fits a series of weak regression models to the dataset sequentially, with each subsequent model focusing on minimizing the errors made by the previous ones.

One key advantage of AdaBoost is its ability to handle noisy data and outliers effectively. By iteratively adjusting the weights of misclassified instances, AdaBoost can downplay the influence of noisy data points, resulting in a more robust model. Additionally, AdaBoost is less prone to overfitting compared with some other algorithms, thanks to its sequential training process and emphasis on misclassified instances.

Gradient regression, often referred to as gradient boosting regression, is a powerful machine learning algorithm used for regression tasks (working principle explained in Figure 3). It belongs to the ensemble learning family, similar to AdaBoost, but with a different approach to model building. Below is an overview of gradient regression and how it was utilized in our study.

Gradient regression builds a predictive model by sequentially adding weak learners, typically decision trees, to an ensemble. However, unlike AdaBoost, which focuses on adjusting the weights of misclassified instances, gradient regression aims to minimize the errors of the previous models by fitting subsequent models to the residuals or errors of the ensemble.

In our study, we employed gradient regression to analyze soil erosion dynamics in Uttar Pradesh, India. Here is how we incorporated gradient regression into our research methodology:

• Algorithm selection: After considering various regression algorithms, including multiple linear regression and AdaBoost regression, we chose gradient regression for its ability to handle complex relationships and improve predictive accuracy through ensemble learning.

• Data preparation: We collected and preprocessed data on soil erosion and its influencing factors in Uttar Pradesh from diverse sources, ensuring its quality and relevance for analysis. The dataset included variables such as slope, land use, precipitation, soil type, vegetation cover, and human activities.

• Model training: We divided the dataset into training and testing subsets and applied gradient regression to model the relationship between soil erosion (dependent variable) and the selected influencing factors (independent variables). We sequentially trained decision tree regressors as weak learners, with each subsequent model focusing on minimizing the errors of the ensemble.

• Parameter tuning: To optimize the performance of the gradient regression model, we conducted parameter tuning through techniques such as grid search or random search. This involved adjusting hyperparameters such as the learning rate, maximum tree depth, and minimum samples per leaf to prevent overfitting and achieve optimal predictive accuracy.

• Evaluation: We evaluated the performance of the gradient regression model using metrics such as mean squared error, R-squared, and root mean squared error. By comparing the model's predictions against the actual soil erosion levels in the testing subset, we assessed its goodness of fit and predictive capability.

• Interpretation: We interpreted the results of the gradient regression analysis to gain insights into the key factors driving soil erosion in Uttar Pradesh. By examining the importance of each variable in the model and their impact on soil erosion levels, we identified actionable strategies for soil erosion mitigation and land management practices.

Overall, gradient regression proved to be an effective tool in our study, enabling us to analyze and predict soil erosion dynamics with high accuracy and providing valuable insights for sustainable land management in Uttar Pradesh.

Our study's findings are based on data collected from specific regions within Uttar Pradesh, which may introduce biases related to local environmental conditions, land-use practices, and data quality. For instance, variations in soil samples and climatic data could affect the generalizability of our results. We acknowledge that while our dataset is comprehensive, it may not fully capture all regional variations or atypical conditions.

The regression models used in our study, including multivariate regression, AdaBoost regression, and gradient boosting regression, are based on several assumptions. For example, multivariate regression assumes linear relationships among variables, which may not fully represent complex, non-linear interactions present in soil erosion dynamics. Although AdaBoost and gradient boosting are capable of handling non-linearity, they still rely on certain assumptions about model behavior and residuals. These assumptions could impact the accuracy of predictions, particularly in scenarios with intricate relationships between predictors.

Extrapolating the findings from our study area to other regions poses challenges due to variations in environmental and socioeconomic conditions. The soil erosion dynamics in Uttar Pradesh may differ significantly from those in other geographical areas with different topographies, land-use patterns, or climatic conditions. Therefore, while our models provide valuable insights into the studied area, their applicability to other regions should be approached with caution. Future research could benefit from applying similar methodologies to diverse locations to validate and refine the findings.

The initial step in our research process involves the selection of the study area. This crucial phase ensures that the area chosen is representative of the geographical and environmental characteristics relevant to the study. It involves a comprehensive analysis of various potential sites, considering factors such as topography, climate, and land-use patterns to identify the most suitable location for data collection and analysis.

• Data collection

Following the selection of the study area, we proceed with data collection. This step involves gathering all necessary data related to soil characteristics, land use, climatic conditions, and other pertinent variables. The data collection process employs various methods, including field surveys, remote sensing, and the use of existing datasets, ensuring the acquisition of accurate and comprehensive data.

• Modeling using three algorithms

The collected data is then subjected to modeling using three distinct regression algorithms: multivariate regression, AdaBoost regression, and gradient boosting regression. Each algorithm is applied independently to the dataset to develop predictive models for soil erosion. These models help in understanding the relationships between the different variables and the extent of soil erosion.

• Multivariate regression: This algorithm models the relationship between multiple independent variables and the dependent variable (soil erosion), providing a linear approximation of the contributing factors.

• AdaBoost regression: This ensemble learning technique improves the accuracy of the model by combining the predictions of multiple weak learners, enhancing the robustness of the model against overfitting.

• Gradient boosting regression: This method builds the model in a stage-wise fashion, optimizing for accuracy by minimizing the error at each stage, thus creating a strong predictive model for soil erosion.

• Consolidating the results

The results from the three modeling techniques are then consolidated. This involves compiling the predictions and performance metrics of each model, enabling a comprehensive comparison and interpretation of the results. Consolidation ensures that all insights derived from the models are taken into account for the subsequent analysis.

• Estimating the dominant cause of soil erosion

With the consolidated results, we estimate the dominant causes of soil erosion in the study area. By analyzing the model outputs and the significance of different variables, we identify the primary factors contributing to soil erosion. This step is crucial for understanding the key drivers and for formulating effective soil conservation strategies.

• Comparing the three algorithms

In this step, we compare the performance of the three algorithms based on various metrics such as accuracy, precision, recall, and computational efficiency. This comparison highlights the strengths and weaknesses of each modeling approach, providing insights into their suitability for soil erosion prediction.

• Finding the best algorithm for the study

Finally, we identify the best algorithm for our study based on the comparison. The selected algorithm is the one that demonstrates the highest predictive accuracy and robustness, offering the most reliable results for understanding and mitigating soil erosion in the study area.

The outcomes of the algorithms are presented in Table 3. This table encompasses coefficients for all 15 parameters identified as significant contributors to soil erosion. These parameters encompass various factors such as soil samples (Soil 1, Soil 2, Soil 3, Soil 4, and Soil 5), land-type classes (urban, forest, barren land, agriculture, and rangeland), slope classes (0–10, 10–20, 20–30, 30–40, and >40), and runoff. The table also provides the standard error for each coefficient. Notably, the highest coefficient values (2.5, 0.49, 0.25, 0.13, and 0.12) correspond to slope >40, barren land, agricultural land, soil classes 3 and 5, respectively. The analysis's R coefficient, or R² value, stands at 0.89 for multivariate regression (which is better than the other two algorithms), indicating a strong correlation.

For our study area where the landscape can include areas with steep slopes and barren land, the following five techniques are particularly suitable for preventing soil erosion:

• (1) Terracing

• • • Description: Creating stepped levels on a slope to slow down water flow and reduce soil erosion.

  • • Benefits: This technique is highly effective in hilly areas around Varanasi, slowing down runoff and allowing for better water infiltration.

  • • Implementation: Local farmers can be trained to build terraces using traditional methods or with the help of simple machinery.

• (2) Contour plowing

• • • Description: Plowing along the contour lines of a slope.

  • • Benefits: Reduces the velocity of water runoff, enhances water infiltration, and minimizes soil erosion.

  • • Implementation: Promoting the practice among local farmers and providing guidance on how to identify and follow contour lines effectively.

• (3) Cover crops

• • • Description: Planting crops such as grasses or legumes to cover the soil during off-seasons.

  • • Benefits: Protects the soil from erosion, improves soil fertility, and adds organic matter to the soil.

  • • Implementation: Introducing suitable cover crops like cowpea or clover that can grow well in the region's climate and soil conditions.

• (4) Revegetation

• • • Description: Planting native grasses, shrubs, and trees on barren land to stabilize the soil.

  • • Benefits: Vegetation provides root systems that bind the soil, reduce runoff, and increase water infiltration.

  • • Implementation: Initiating community-based programs to plant native species that are adapted to the local environment, such as banyan trees, neem, and vetiver grass.

• (5) Gully plugging

• • • Description: Filling gullies with stones, vegetation, or other materials to slow water flow and prevent further erosion.

  • • Benefits: Stabilizes gullies, reduces runoff speed, and promotes sediment deposition.

  • • Implementation: Local authorities and communities can collaborate to identify and plug gullies using locally available materials like stones and plant debris.

• (1) Community involvement: Engage local communities through awareness programs about the benefits of these techniques. Involving farmers and residents in the planning and execution can ensure sustainable practices.

• (2) Training and support: Provide training workshops for farmers and landowners on how to implement these techniques effectively. Government and non-government organizations can offer technical support and resources.

• (3) Government policies and incentives: Advocate for policies that support soil conservation practices, including subsidies or financial incentives for farmers who adopt these methods.

• (4) Monitoring and maintenance: Establish a monitoring system to regularly assess the effectiveness of the implemented techniques and make necessary adjustments. Encourage community participation in maintenance activities.

• (5) Pilot projects: Start with pilot projects in areas most affected by soil erosion to demonstrate the effectiveness of these techniques, which can then be scaled up to other regions.

By focusing on these five techniques, Varanasi can effectively combat soil erosion, protect agricultural lands, and promote sustainable land management practices.

In conclusion, this paper demonstrates the efficacy of machine learning algorithms in identifying and understanding the complex drivers of soil erosion in the Varanasi region. By comparing three distinct algorithms, we have not only pinpointed slope as the predominant factor contributing to erosion but also showcased multivariate regression's superior predictive accuracy. This highlights our research's innovation in employing advanced statistical methods tailored to the region's specific environmental dynamics.

Looking ahead, our findings pave the way for several future avenues of exploration. Firstly, further research could delve into refining machine learning models by incorporating additional variables and exploring more advanced algorithmic techniques. Additionally, expanding the scope of the study to encompass neighboring regions could provide broader insights into soil erosion dynamics in similar agroecological contexts.

Moreover, our study sets a benchmark for the integration of interdisciplinary approaches, emphasizing the importance of combining machine learning methodologies with domain-specific knowledge in environmental science and agriculture. This holistic approach not only enhances the accuracy of predictive models but also ensures the relevance and applicability of research findings in real-world conservation efforts.

In essence, our research not only advances the understanding of soil erosion dynamics in Varanasi but also serves as a blueprint for future studies seeking to address environmental challenges through innovative methodologies and interdisciplinary collaboration. By continually refining our approaches and embracing emerging technologies, we can strive towards more effective and sustainable solutions for soil conservation on a global scale.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.