Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Abstract

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

“…e first set is used for model training and the second set is used to inspect the model predictive capability. In addition, to standardize the input data range, the Z-score equation is used as follows [96]:…”

“…To optimize the SVM model used for urban green space detection, the objective function of the MPA metaheuristic has employed a 5-fold cross validation process and the indices of false negative rate (FNR) and false positive rate (FPR). is objective function (OF) is described as follows [96]:…”

“…where FNR k and FPR k denote FNR and FPR computed in the kth run, respectively. e FNR and FPR indices are given by [96] FNR…”

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

“…It is noted that the task of searching for those hyperparameters can be considered as a global optimization problem [28,32,[64][65][66][67][68][69][70][71]. Moreover, since C and σ are searched in continuous space, the number of parameter combinations is infinitely large.…”

A Comparative Empirical Analysis of 21 Machine Learning Algorithms for Real-World Applications in Diverse Domains

“…Machine Learning Algorithms (MLAs) have been applied in several domains to help augment human efforts to reduce some of the challenges posed by earlier technologies. Some of these domains include estimating soil cohesion and rainfall-induced soil erosion [9], [10], rainfall and flood forecasting [11]- [14], early heart disease and heart rate detection [15]- [18], financial projection and fraud detection [5], [6], [19], [20], defects in conductors and semiconductors manufacturing [21], and bike-rental estimation [22]. MLAs can be grouped as (i) supervised learning, (ii) semi-supervised learning, (iii) unsupervised learning, and (iv) reinforcement learning.…”