Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India

Roy, Paramita; Chakrabortty, Rabin; Chowdhuri, Indrajit; Malik, Sadhan; Das, Biswajit; Pal, Subodh Chandra

doi:10.1007/978-981-15-3689-2_1

Cited by 42 publications

(31 citation statements)

References 47 publications

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“…Thus, it is very much essential to analyze the multi-collinearity of a model to obtain better results through removing the high multi-collinearity factors and minimizing the bias of the model [ 37 ]. Several researchers throughout the world have used multi-collinearity analysis in different fields such as GES mapping [ 21 ], floods [ 38 ], and landslide susceptibility mapping [ 39 ]. Multi-collinearity can be analyzed through variance inflation factor (VIF) and tolerance (TOL) [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…Some of the widely used statistical methods to predict GES mapping are frequency ratio [ 7 ], logistic regression [ 18 ], weight of evidence (WoE) [ 19 ], index of entropy (IoE) [ 5 ], etc. Besides statistical methods, different ML algorithms have also been widely used to predict GES mapping such as artificial neural network (ANN) [ 20 ], support vector machine (SVM) [ 20 ], random forest (RF) (Hosseinalizadeh et al 2019), multi-layer perception (MLPC) approaches [ 21 ], classification and regression tree (CART) [ 22 ], boosted regression tree (BRT) [ 7 ], particle swarm optimization (PSO) [ 23 ], multi-variate adaptive regression spline (MARS) [ 5 ], and maximum entropy [ 24 ]. Ensemble models have also been widely used for their novelties and capabilities in the comprehensive analysis of GES mapping [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Band

Janizadeh

Pal

et al. 2020

Sensors

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129

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This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Band

Janizadeh

Pal

et al. 2020

Sensors

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129

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“…Most importantly, knowledge-based MCDA [11] and statistical analysis based on continuous, binary, and categorical data, such as the information value [23,35], conditional probability [36], certainty factor [37], frequency ratio [38], evidential belief function [2], index of entropy (IoE) [39], weights of evidence (WoE) [40], and logistic regression [2,41], has been widely used by several researchers. In the case of the machine learning algorithm, the most successful models for GESM are the multi-layer perception approach (MLPC) [42], multivariate adaptive regression spline (MARS) [39], artificial neural network (ANN) [43], classification and regression trees (CART) [23], maximum entropy (ME) [44], decision tree (DT) [45], boosted regression tree [15], stochastic gradient treeboost (SGT) [46], random forest (RF) [47], bagging best-first decision tree [48], general linear model (GLM) [49], maximum entropy [50], etc. In general, GESM with machine learning models is more proficient at predicting susceptible areas than statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

et al. 2020

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The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.

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“…Thus, it is very much essential to analyze the multi-collinearity of a model to get better result through removing the high multi-collinearity factors and minimize the bias of the model [39]. Several researchers throughout the world have been used multi-collinearity analysis in different fields such as GSE mapping [22], flood [40], landslide susceptibility mapping [41] etc. Multi-collinearity can be analyzed through variance inflation factor (VIF) and tolerance (TOL) [42].…”

Section: Multi-collinearity Analysismentioning

confidence: 99%

“…Some of the widely used statistical methods to predict GES mapping are frequency ratio [7], logistic regression [18], weight of evidence (WoE) [19], index of entropy (IoE) [5] etc. Beside statistical methods, different ML algorithm have also been widely used to predict GES mapping such as artificial neural network (ANN) [20], support vector machine (SVM) [21], random forest (RF) (Hosseinalizadeh et al 2019), multi-layer perception approach (MLPC) [22], classification and regression tree (CART) [23], boosted regression tree (BRT) [7], particle swarm optimization (PSO) [24], multivariate adaptive regression spline (MARS) [5], maximum entropy [25] etc. Beside this, Ensemble Models has also been widely used for its novelties and capabilities in the comprehensive analysis of GES mapping [26].…”

Section: Introductionmentioning

confidence: 99%

Novel Ensemble Approach of Deep Learning Neural Network Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Shahab¹,

Janizadeh²,

Pal³

et al. 2020

Preprint

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This study aims to evaluate a new approach in modeling gully erosion susceptibility based on deep learning neural network (DLNN) model, ensemble Particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN) and comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shiran watershed, Iran. For this purpose, 13 independent variables affecting gully erosion susceptibility in the study area, including altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from river, land use, soil, lithology, rainfall, , stream power index (SPI), topographic wetness index (TWI), were prepared. Also, 132 gully erosion locations were identified during field visits. Data for modeling were divided into two categories of training (70%) and testing (30%). Receiver operating characteristic (ROC) parameters including sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and area under curve (AUC) were used to evaluate the performance of the models. The results showed that, the AUC values from ROC with considering testing datasets of PSO-DLNN is 0.89 and which is associated with superb accuracy. Rest of the models also associated with optimal accuracy and near about PSO-DLNN model; the AUC values from ROC of DLNN, SVM and ANN for testing datasets are 0.87, 0.85 and 0.84 respectively. The PSO algorithm has updated and optimized the weights of DLNN model, and as a result, the efficiency of this model in predicting gully erosion susceptibility has increased. Therefore, it can be concluded that the use of DLNN model and its ensemble with PSO algorithm can be used as a novel and practical method in predicting the susceptibility of gully erosion that helps planners and managers in managing and reducing the risk of this phenomenon.

show abstract

Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India

Cited by 42 publications

References 47 publications

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

Novel Ensemble Approach of Deep Learning Neural Network Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

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