Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India

Saha, Sunil; Paul, Goutam; Pradhan, Biswajeet; Maulud, Khairul Nizam Abdul; Alamri, Abdullah

doi:10.1080/19475705.2020.1860139

Cited by 26 publications

(12 citation statements)

References 91 publications

(113 reference statements)

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“…Landslides analysis using data mining techniques to produce regions of landslides susceptibility from GIS data has been an essential tool in regional planning and management (Hong et al 2017). In addition, literature has proven that the data mining technique produces landslides susceptibility maps of high predictive accuracies that tackled real-life landslides scenarios (Ma and Xu 2019;Nhu et al 2020c;Saha et al 2021). Although, it is still challenging to produce high accuracy landslide models from the technique in various places due to the dynamism of landslides and the factors involved (Tien Bui et al, 2017b;Tien et al 2020;Balogun et al 2021).…”

Section: Resultsmentioning

confidence: 99%

“…Overall, the accuracy of the models developed using this technique has shown high prediction performance and high success rates (Ayodele 2010;Oladipupo 2012;Goetz et al 2015;Dickson and Perry 2016;Shirzadi et al 2018;Ghorbanzadeh et al 2019;Hegde and Rokseth 2020). The dynamic nature of landslides with their conditioning and triggering factors across different locations made researchers explore different algorithms to harness the maximum prediction rate from the soft computing techniques (Chen X and Chen W 2021;Diana et al 2021;Saha et al 2021;Youssef and Pourghasemi 2021). To date, many researchers are using machine learning algorithms to mine data and make valuable predictions of landslide occurrence effectively.…”

Section: Introductionmentioning

confidence: 99%

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Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

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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

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

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“…As was demonstrated in the previous works related to the natural hazards susceptibility assessment, the parameters of the present paper's models have a significant influence on the final results obtained. For example, in the case of Multilayer Perceptron Neural Network, a strong impact in the results accuracy is held by the back-propagation function, whose main scope is to reduce as much as possible the classification error between during the training phase (Saha et al 2021). Moreover, for the SVM algorithm, the model complexity and precision is an influence in a high measure by the cost parameter (Oh et al 2018), while the Random Forest model accuracy is highly determined by the hyperparameters represented by a specific number of decision trees (Lee et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Detection of areas prone to flood risk using state-of-the-art machine learning models

Costache

Arabameri

Elkhrachy

et al. 2021

Geomatics, Natural Hazards and Risk

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The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC ¼ 0.996).

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“…Therefore, scientists now address these problems by forming hybrids of different machine learning models together. Ensemble or hybrid machine learning models showed high accuracy and better performance than conventional methods in many previous studies (Pham et al 2017;Saha et al 2021).…”

Section: Introductionmentioning

confidence: 87%

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Yaseen

Chen

2022

Stoch Environ Res Risk Assess

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Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flooding are different for every region as they depend upon the characteristics of each region. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of the ensemble model showed 99% accuracy in training and 98% accuracy in validation datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.

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Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India

Cited by 26 publications

References 91 publications

Advanced data mining techniques for landslide susceptibility mapping

Advanced data mining techniques for landslide susceptibility mapping

Detection of areas prone to flood risk using state-of-the-art machine learning models

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

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