Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

Balogun, Abdul‐Lateef; Rezaie, Fatemeh; Pham, Quoc Bao; Gigović, Ljubomir; Drobnjak, Siniša; Aina, Yusuf A.; Panahi, Mahdi; Yekeen, Shamsudeen Temitope; Lee, Saro

doi:10.1016/j.gsf.2020.10.009

Cited by 118 publications

(46 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where γ and b are vector weights and bias, respectively, where γ, b ∈ R. The dot product between the input E and weight vector γ is represented by γ•E . Restricting the precision of the model to a threshold value defined by epsilon ε requires that the Euclidean norm shown in Equation ( 2) is minimized and subjected to the constraints and conditions of Equation (3) [29,30].…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…The dot product between the input

and weight vector

is represented by

. Restricting the precision of the model to a threshold value defined by epsilon

requires that the Euclidean norm shown in Equation (2) is minimized and subjected to the constraints and conditions of Equation (3) [ 29 , 30 ].

where measured and estimated refractive indices are denoted by

and

, respectively.…”

Section: Mathematical Descriptions Of the Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Owolabi

Rahman

2021

Polymers

View full text Add to dashboard Cite

We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced.

show abstract

Section: Support Vector Regressionmentioning

confidence: 99%

“…The dot product between the input

and weight vector

is represented by

. Restricting the precision of the model to a threshold value defined by epsilon

requires that the Euclidean norm shown in Equation (2) is minimized and subjected to the constraints and conditions of Equation (3) [ 29 , 30 ].

where measured and estimated refractive indices are denoted by

and

, respectively.…”

Section: Mathematical Descriptions Of the Algorithmsmentioning

confidence: 99%

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Owolabi

Rahman

2021

Polymers

View full text Add to dashboard Cite

show abstract

“…Due to data quality, factor selection, model parameter adjustment and other factors, some low accuracy, over fitting, and owe fitting problems often appear. In order to solve these problems, hybrid model was developed in recent years, such as reduced error pruning trees (REPT) (Pham et al, 2019b), kernel logistic regression model integrated with fractal dimension (KLRbox-counting) (Zhang et al, 2019), support vector regression model integrated with gray wolf optimization algorithm (SVR-GWO) (Balogun et al, 2021), adaptive neuro-fuzzy inference system model integrated with satin bowerbird optimizer algorithms (ANFIS-SBO) (Chen et al, 2021). Although several models listed above have been previously applied in assessment field of landslide susceptibility and performed well, applying these models to forecast landslide occurrence and explore how to raise prediction accuracy are still the focus of current researches.…”

Section: Instructionmentioning

confidence: 99%

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

Zhang¹,

Wang

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

The losses and damage caused by landslides are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were respectively prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.

show abstract

“…Chowdhuri et al [32] introduced hybrid models from statistical and machine learning model integrations for predicting spatially the landslide occurrence in a basin of India. In addition, some studies have improved the performances of machine learning models by combining them with optimization or meta-heuristic algorithms [33,34].…”

Section: Introductionmentioning

confidence: 99%

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Polykretis

Grillakis

Argyriou

et al. 2021

Land

View full text Add to dashboard Cite

Over the last few years, landslides have occurred more and more frequently worldwide, causing severe effects on both natural and human environments. Given that landslide susceptibility (LS) assessments and mapping can spatially determine the potential for landslides in a region, it constitutes a basic step in effective risk management and disaster response. Nowadays, several LS models are available, with each one having its advantages and disadvantages. In order to enhance the benefits and overcome the weaknesses of individual modeling, the present study proposes a hybrid LS model based on the integration of two different statistical analysis models, the multivariate Geographical Detector (GeoDetector) and the bivariate information value (IV). In a GIS-based framework, the hybrid model named GeoDIV was tested to generate a reliable LS map for the vicinity of the Pinios artificial lake (Ilia, Greece), a Greek wetland. A landslide inventory of 60 past landslides and 14 conditioning (morphological, hydro-lithological and anthropogenic) factors was prepared to compose the spatial database. An LS map was derived from the GeoDIV model, presenting the different zones of potential landslides (probability) for the study area. This map was then validated by success and prediction rates—which translate to the accuracy and prediction ability of the model, respectively. The findings confirmed that hybrid modeling can outperform individual modeling, as the proposed GeoDIV model presented better validation results than the IV model.

show abstract

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

Cited by 118 publications

References 99 publications

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Contact Info

Product

Resources

About