Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Tuan Vu Dinh,Nhat-Duc Hoang,Xuan-Linh Tran,Maman Turjaman

doi:10.1155/2021/6665485

Abstract

Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.

Highlights

Water erosion often causes loss of soil from the field, breakdown of soil structure, and decline of organic matter and nutrients [1]
Soil erosion potential in tropical areas is high due to heavy rainfall coupled with land management such as mono cropping in the uplands of Northwest Vietnam [4, 5]
A relevance vector machine (RVM) model often results in good predictive performance thanks to its sparseness property. It is because a RVM model relies on a small number of relevant vectors extracted from the training samples to construct the classification model [31]

Summary

Introduction

Water erosion often causes loss of soil from the field, breakdown of soil structure, and decline of organic matter and nutrients [1]. Is study elucidates potential application of five competent machine learning models to predict soil erosion: artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), relevance vector machine (RVM), and fuzzy k-nearest neighbor (FKNN) using a dataset containing ten explanatory variables, collected from fields in Northwest Vietnam. The FKNN [33] is an extension of the standard k-nearest neighbor (KNN) algorithm; this model incorporates the fuzzy theory into the KNN model structure to enhance the flexibility of data modeling and better constructs the class decision boundary Due to such characteristics and advantages, these five models are selected to be employed in this study

Research Methodology

Machine Learning Methods for Soil Erosion Status Prediction

Results and Discussion