Soil Erosion Status Prediction Using a Novel Random Forest Model Optimized by Random Search Method

Zahraa Tarek, Ahmed M. Elshewey, Samaa M. Shohieb (), Abdelghafar M. Elhady, Noha E. El-Attar, Sherif Elseuofi and Mahmoud Y. Shams
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Zahraa Tarek: Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt
Ahmed M. Elshewey: Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt
Samaa M. Shohieb: Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt
Abdelghafar M. Elhady: Deanship of Scientific Research, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Noha E. El-Attar: Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
Sherif Elseuofi: Information System Department, Higher Institute for Computers & Specific Studies, Ras El-Bar, Damietta 34711, Egypt
Mahmoud Y. Shams: Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt

Sustainability, 2023, vol. 15, issue 9, 1-18

Abstract: 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%.

Keywords: soil erosion; random forest; random search; classification; evaluation metrics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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