Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi‑collinearity analysis and K-fold cross-validation

Ghorbanzadeh, Omid; Shahabi, Hejar; Mirchooli, Fahimeh; Kamran, Khalil Valizadeh; Lim, Samsung; Aryal, Jagannath; Jarihani, Ben; Blaschke, Thomas

doi:10.1080/19475705.2020.1810138

Cited by 44 publications

(11 citation statements)

References 64 publications

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“…We emphasize that the zoning of interventions within erosions was only possible with the vectorization of the drainage lines through visual inspection. Although the current trend of applying machine learning algorithms dominates publications on the spatialization of erosive processes (Ghorbanzadeh et al, 2020) or the detection of ravine edges (Li et al, 2021), here we highlight the importance of human expertise on RS images analysis. In this sense, our algebra of selection and exclusion of false positives can be useful as an attribute for achieving more robust models (Minella et al, 2010), or even for integrating variables in GIS, such as distance to roads, lithology, slope curvatures, and topographic indices (Zabihi et al, 2018;Pourghasemi et al, 2020;Amiri and Pourghasemi, 2020), especially regarding the locations suitable for palisades, whose studies are still scarce.…”

Section: Methodological Challengesmentioning

confidence: 93%

Priority setting for restoration in surrounding savannic areas of the Brazilian Pantanal based on soil loss risk and agrarian structure

Louzada

Bergier

Diniz

et al. 2022

Journal of Environmental Management

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Section: Methodological Challengesmentioning

confidence: 93%

Priority setting for restoration in surrounding savannic areas of the Brazilian Pantanal based on soil loss risk and agrarian structure

Louzada

Bergier

Diniz

et al. 2022

Journal of Environmental Management

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“…Compared to the conventional approaches for soil erosion prediction such as the Revised Universal Soil Loss Equation (RUSLE) [124,125] which requires significant Mathematical Problems in Engineering efforts on parameter calibration, the newly proposed method is entirely data-driven in which all of the model parameters are determined via the model training process. In addition, recently proposed machine learning methods for soil erosion susceptibility prediction have dominantly relied on individual or ensemble of models [126][127][128]. erefore, trialand-error processes and modeling experience are required for constructing such machine learning models.…”

Section: Discussionmentioning

confidence: 99%

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

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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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%).

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“…Multicollinearity is a notion in statistics where multiple variables in a model are associated with each other, i.e., correlation. When the correlation coefficient is negative or positive, the variable is collinear in nature (Ghorbanzadeh et al, 2020).This led to the consequence of less dependable statistical hypotheses. So, to check that, we have used the Chisquare test and VIF.…”

Section: Data Cleaningmentioning

confidence: 99%

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Divyasri¹,

Saranya

P.Kathiravan

2023

Preprint

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Crimes are a social issue that affects not only an individual but also humanity. Crime classification techniques for crime forecasting are an emerging research area. generally, Crime data are centrally organized with regular maintenance of the criminal registers that can aid officers in sharing observations and improve early alert approaches to keep the citizens secure within their towns. Hence, the aim of this study is to compare the performance of the state-of-the-art Dynamic Ensemble Selection of Classifier algorithms for predicting crime. We used five different benchmark crime datasets (Chicago, San Francisco, Pheonix, Boston, and Vancouver) for this experimental research work. The performance of the state-of-the-art dynamic ensemble selection of classifiers algorithms was evaluated and compared using various performance evaluation metrics such as accuracy, F1-score, precision, and recall. The KNORA Dynamic ensemble algorithms, which select the subset of ensemble members before the forecasting, outperformed the typical machine learning algorithms, and also the traditional ensemble algorithm techniques in terms of accuracy showed that the dynamic ensemble algorithms are more powerful. This ability to predict crimes within urban societies can help citizens, and law enforcement makes precise informed conclusions and preserves the neighborhoods more unassailably to improve the quality of life for humans.

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Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi‑collinearity analysis and K-fold cross-validation

Abstract: Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi-collinearity analysis and K-fold cross-validation,

Cited by 44 publications

References 64 publications

Priority setting for restoration in surrounding savannic areas of the Brazilian Pantanal based on soil loss risk and agrarian structure

Priority setting for restoration in surrounding savannic areas of the Brazilian Pantanal based on soil loss risk and agrarian structure

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

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

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