Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms

We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.

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Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

Remote Sensing ◽

10.3390/rs12172742 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2742

Author(s):

Ehsan Kamali Maskooni ◽

Seyed Amir Naghibi ◽

Hossein Hashemi ◽

Ronny Berndtsson

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Logistic Model ◽

Nearest Neighbors ◽

Driving Factors ◽

Machine Learning Algorithms ◽

Boosted Regression Trees ◽

K Nearest Neighbors ◽

Model Tree ◽

Logistic Model Tree

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

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Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree

Geocarto International ◽

10.1080/10106049.2019.1588393 ◽

2019 ◽

Vol 34 (11) ◽

pp. 1177-1201 ◽

Cited By ~ 37

Author(s):

Wei Chen ◽

Xia Zhao ◽

Himan Shahabi ◽

Ataollah Shirzadi ◽

Khabat Khosravi ◽

...

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Logistic Model ◽

Spatial Prediction ◽

Belief Function ◽

Evidential Belief Function ◽

Model Tree ◽

Logistic Model Tree

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Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17082749 ◽

2020 ◽

Vol 17 (8) ◽

pp. 2749 ◽

Cited By ~ 18

Author(s):

Viet-Ha Nhu ◽

Ataollah Shirzadi ◽

Himan Shahabi ◽

Sushant K. Singh ◽

Nadhir Al-Ansari ◽

...

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Landslide Susceptibility ◽

Logistic Model ◽

Naive Bayes ◽

Shallow Landslide ◽

Naïve Bayes ◽

Support Vector ◽

Model Tree ◽

Logistic Model Tree

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

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A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

CATENA ◽

10.1016/j.catena.2016.11.032 ◽

2017 ◽

Vol 151 ◽

pp. 147-160 ◽

Cited By ~ 255

Author(s):

Wei Chen ◽

Xiaoshen Xie ◽

Jiale Wang ◽

Biswajeet Pradhan ◽

Haoyuan Hong ◽

...

Keyword(s):

Random Forest ◽

Comparative Study ◽

Landslide Susceptibility ◽

Logistic Model ◽

Regression Tree ◽

Spatial Prediction ◽

Classification And Regression Tree ◽

Tree Models ◽

Logistic Model Tree ◽

Classification And Regression

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Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Water ◽

10.3390/w11081596 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1596 ◽

Cited By ~ 12

Author(s):

S. Vahid Razavi-Termeh ◽

Abolghasem Sadeghi-Niaraki ◽

Soo-Mi Choi

Keyword(s):

Data Mining ◽

Random Forest ◽

Statistical Models ◽

Logistic Model ◽

Characteristic Curve ◽

Groundwater Potential ◽

Model Tree ◽

Potential Map ◽

Groundwater Potential Map ◽

Logistic Model Tree

In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m3/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map.

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A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment

Geocarto International ◽

10.1080/10106049.2018.1425738 ◽

2018 ◽

Vol 33 (12) ◽

pp. 1398-1420 ◽

Cited By ~ 58

Author(s):

Wei Chen ◽

Himan Shahabi ◽

Ataollah Shirzadi ◽

Tao Li ◽

Chen Guo ◽

...

Keyword(s):

Landslide Susceptibility ◽

Logistic Model ◽

Susceptibility Assessment ◽

Landslide Susceptibility Assessment ◽

Ensemble Approach ◽

Model Tree ◽

Tree Classifier ◽

Logistic Model Tree

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Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree

Applied Sciences ◽

10.3390/app8071046 ◽

2018 ◽

Vol 8 (7) ◽

pp. 1046 ◽

Cited By ~ 32

Author(s):

Xuan Truong ◽

Muneki Mitamura ◽

Yasuyuki Kono ◽

Venkatesh Raghavan ◽

Go Yonezawa ◽

...

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Logistic Model ◽

Prediction Performance ◽

Learning Approach ◽

Machine Learning Approach ◽

Susceptibility Model ◽

Logistic Model Tree ◽

Hybrid Machine ◽

Bagging Ensemble

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Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions

Neurosurgical FOCUS ◽

10.3171/2021.2.focus201113 ◽

2021 ◽

Vol 50 (5) ◽

pp. E5

Author(s):

Elie Massaad ◽

Natalie Williams ◽

Muhamed Hadzipasic ◽

Shalin S. Patel ◽

Mitchell S. Fourman ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Hospital Mortality ◽

Predictive Value ◽

Validation Cohort ◽

Frailty Index ◽

Spinal Tumor ◽

Machine Learning Algorithms ◽

Metastatic Spinal Tumor

OBJECTIVE Frailty is recognized as an important consideration in patients with cancer who are undergoing therapies, including spine surgery. The definition of frailty in the context of spinal metastases is unclear, and few have studied such markers and their association with postoperative outcomes and survival. Using national databases, the metastatic spinal tumor frailty index (MSTFI) was developed as a tool to predict outcomes in this specific patient population and has not been tested with external data. The purpose of this study was to test the performance of the MSTFI with institutional data and determine whether machine learning methods could better identify measures of frailty as predictors of outcomes. METHODS Electronic health record data from 479 adult patients admitted to the Massachusetts General Hospital for metastatic spinal tumor surgery from 2010 to 2019 formed a validation cohort for the MSTFI to predict major complications, in-hospital mortality, and length of stay (LOS). The 9 parameters of the MSTFI were modeled in 3 machine learning algorithms (lasso regularization logistic regression, random forest, and gradient-boosted decision tree) to assess clinical outcome prediction and determine variable importance. Prediction performance of the models was measured by computing areas under the receiver operating characteristic curve (AUROCs), calibration, and confusion matrix metrics (positive predictive value, sensitivity, and specificity) and was subjected to internal bootstrap validation. RESULTS Of 479 patients (median age 64 years [IQR 55–71 years]; 58.7% male), 28.4% had complications after spine surgery. The in-hospital mortality rate was 1.9%, and the mean LOS was 7.8 days. The MSTFI demonstrated poor discrimination for predicting complications (AUROC 0.56, 95% CI 0.50–0.62) and in-hospital mortality (AUROC 0.69, 95% CI 0.54–0.85) in the validation cohort. For postoperative complications, machine learning approaches showed a greater advantage over the logistic regression model used to develop the MSTFI (AUROC 0.62, 95% CI 0.56–0.68 for random forest vs AUROC 0.56, 95% CI 0.50–0.62 for logistic regression). The random forest model had the highest positive predictive value (0.53, 95% CI 0.43–0.64) and the highest negative predictive value (0.77, 95% CI 0.72–0.81), with chronic lung disease, coagulopathy, anemia, and malnutrition identified as the most important predictors of postoperative complications. CONCLUSIONS This study highlights the challenges of defining and quantifying frailty in the metastatic spine tumor population. Further study is required to improve the determination of surgical frailty in this specific cohort.

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Klasifikasi Jenis Pemeliharaan dan Perawatan Container Crane menggunakan Algoritma Machine Learning

MATICS ◽

10.18860/mat.v13i1.11525 ◽

2021 ◽

Vol 13 (1) ◽

pp. 21-27

Author(s):

Via Ardianto Nugroho ◽

Derry Pramono Adi ◽

Achmad Teguh Wibowo ◽

MY Teguh Sulistyono ◽

Agustinus Bimo Gumelar

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Decision Tree ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Container Crane ◽

Model Tree

Pada industri jasa pelayanan peti kemas, Terminal Nilam merupakan pelanggan dari PT. BIMA, yang secara khusus bergerak dibidang jasa perbaikan dan perawatan alat berat. Terminal ini menjadi sentral tempat untuk melakukan aktifitas bongkar muat peti kemas domestik yang memiliki empat buah container crane untuk melayani dua kapal. Proses perawatan alat berat seperti container crane yang selama ini beroperasi, agaknya kurang memperhatikan data pengelompokkan atau klasifikasi jenis perawatan yang dibutuhkan oleh alat berat tersebut. Di kemudian hari, alat berat dapat menunjukkan kinerja yang tidak maksimal bahkan dapat berujung pada kecelakaan kerja. Selain itu, kelalaian perawatan container crane juga dapat menyebabkan pembengkakan biaya perawatan lanjut. Target produksi bongkar muat dapat berkurang dan juga keterlambatan jadwal kapal sandar sangat mungkin terjadi. Metode pembelajaran menggunakan mesin atau biasa disebut dengan Machine Learning (ML), dengan mudah dapat melenyapkan kemungkinan-kemungkinan tersebut. ML dalam penelitian ini, kami rancang agar bekerja dengan mengidentifikasi lalu mengelompokkan jenis perawatan container crane yang sesuai, yaitu ringan atau berat. Metode ML yang pilih untuk digunakan dalam penelitian ini yaitu Random Forest, Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, Logistic Regression, J48, dan Decision Tree. Penelitian ini menunjukkan keberhasilan ML model tree dalam melakukan pembelajaran jenis data perawatan container crane (numerik dan kategoris), dengan J48 menunjukkan performa terbaik dengan nilai akurasi dan nilai ROC-AUC mencapai 99,1%. Pertimbangan klasifikasi kami lakukan dengan mengacu kepada tanggal terakhir perawatan, hour meter, breakdown, shutdown, dan sparepart.

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