article · Sustainability
Soil erosion, a process of soil particle dislocation, transport, and deposition, is influenced by factors such as soil type and land cover. Accurate prediction of this phenomenon is essential for effective soil conservation. This research introduces the Random Search-Random Forest (RS-RF) model, which integrates random search optimisation with the Random Forest algorithm, to predict soil erosion status. The model was trained on a dataset of 236 instances with 11 features, classifying areas as eroding or non-eroding. Evaluated using six metrics, including accuracy and F1-score, the RS-RF model achieved a 97.4% accuracy rate, outperforming other machine learning techniques and previous studies on the same dataset.
Accurate prediction of soil erosion is vital for protecting land resources and maintaining ecosystem health. This research offers an improved tool for identifying areas at risk, enabling better planning for soil conservation and sustainable land management. This helps prevent environmental degradation and supports agricultural productivity.
This research presents an applied machine learning model for predicting soil erosion, which could be used by environmental agencies, agricultural organisations, or land management companies. It offers a decision-support tool to identify areas requiring conservation efforts, aiding in resource allocation and planning. The model's high accuracy suggests it is a robust solution, potentially near-market for integration into existing land management software or services.
AI-generated from the published abstract. Always read the original work before citing.
Soil erosion, the degradation of the earth’s surface through the removal of soil particles, occurs in three phases: dislocation, transport, and deposition. Factors such as soil type, assembly, infiltration, and land cover influence the velocity of soil erosion. Soil erosion can result in soil loss in some areas and soil deposition in others. In this paper, we proposed the Random Search-Random Forest (RS-RF) model, which combines random search optimization with the Random Forest algorithm, for soil erosion prediction. This model helps to better understand and predict soil erosion dynamics, supporting informed decisions for soil conservation and land management practices. This study utilized a dataset comprising 236 instances with 11 features. The target feature’s class label indicates erosion (1) or non-erosion (−1). To assess the effectiveness of the classification techniques employed, six evaluation metrics, including accuracy, Matthews Correlation Coefficient (MCC), F1-score, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC), were computed. The experimental findings illustrated that the RS-RF model achieved the best outcomes when compared with other machine learning techniques and previous studies using the same dataset with an accuracy rate of 97.4%.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/su15097114
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
