Logistic regression and fuzzy logic as a classification method for feral fish sampling sites

Hauser‐Davis, Rachel Ann; Oliveira, Tatianne Ferreira de; Silveira, Antonio Morais da; Protázio, João Marcelo Brazão; Ziolli, Roberta Lourênço

doi:10.1007/s10651-012-0196-1

Cited by 5 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical models best correlate the dynamism in landslides between many predisposing factors and landslides (Balogun et al 2021). To this effect, many statistical models such as frequency ratio (Guzzetti et al 1999;Gorsevski et al 2006), fuzzy logic (Akgun et al 2012;Balal and Cheu 2018;Chen W et al 2017a;Hauser-Davis et al 2012;Irvin et al 1997;Tien Bui et al, 2017a), the weight of evidence models (Chang M et al 2020;Lee JH et al 2018;Pamela et al 2018;Polykretis and Chalkias 2018), logistic regressions (Bai et al 2010;Chen W et al 2017b;Chen W et al 2019;Pradhan 2010), analytical hierarchy process (Althuwaynee et al 2014;Saadatkhah et al 2014;Mardani et al 2015;Asadabadi et al 2017) and many more have all been utilized to produce susceptibility models. More recently, soft computing procedures (Data mining) are increasingly being adopted for landslide susceptibility analysis due to their impressive performance.…”

Section: Introductionmentioning

confidence: 99%

Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

This paper describes the development and validation of landslides susceptibility models for mountainous regions using advanced data mining techniques. The investigation was carried out to ascertain the effectiveness of Naïve Bayes Multinomial (NBM) and Random Trees (RT) in landslide susceptibility mapping. The NBM is an advancement of the frequently used Naïve Bayes classifiers, while the RT was built to overcome the limitations of the traditional forest classifiers. A geospatial database for this investigation comprises 148 landslide locations influenced by ten ( 10) landslide conditioning factors. The factors (Slope Angle, Slopes Elevation, Slope Aspect, Plan curvature, Profile Curvature, Lithology, Soil type, Stream power index (SPI), Sediment transport index (STI), and Rainfall precipitation) were drawn using a Multi Collinearity Decision Making (MCDM) technique. A Frequency Ratio (FR) analysis was used to obtain the relative significance of the factors in the slides. Predictive models were also developed by quantifying these models using data mining techniques. A section of the entire geospatial data (70%) was used as training datasets, while the remaining part of the data (30%) was used to validate the trained datasets. SVM, RT, and NBM algorithms were used to produce predicted datasets from the training datasets. These predicted datasets were used to develop the Landslides Susceptibility Models. A comparative assessment between the two classifiers against the famous traditional learning algorithm, the Support vector machines (SVM), was conducted. Model performance evaluators such as the AUROC, RSME, F-measure, MAE, and ACC were employed to check the predictive capabilities and accuracies of the models. The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. Further analysis and comparison of the models reveal that the new data mining techniques are reliable for landslide susceptibility. Simultaneously, the traditional algorithm is also useful and remains relevant, especially with similar site conditions. This study has provided insights on better ARTICLE HISTORY

show abstract