Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

Hong, Haoyuan; Pradhan, Biswajeet; Sameen, Maher Ibrahim; Chen, Wei; Xu, Chong

doi:10.1080/19475705.2017.1403974

Cited by 53 publications

(33 citation statements)

References 99 publications

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“…Too low resolution cannot guarantee the rationality of the obtained LSP, while too high resolution will greatly increase the model computation complexity [62]. Although some studies focusing on the issue of LSP considering the different spatial resolutions of grid units, show that the LSP performance decreases when the resolution of grid units rises from 10 m to 100 m [63], a lot of literature shows that a 30 m grid resolution is suitable for LSP and can obtain satisfactory LSP results [7,11,21,30,35,37,50,59,[64][65][66][67][68][69]. Meanwhile, the original grid resolutions of the DEM and remote sensing images used in this study are both 30 m, which can not only effectively represent the topographic characteristics, but also avoid excessive computation.…”

Section: Sensitivity Analysis On Resolution Of Grid Unitsmentioning

confidence: 99%

Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

et al. 2020

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Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS). However, comparisons of the applications of ML models for LSP from the perspectives of supervised machine learning (SML) and unsupervised machine learning (USML) have not been explored. Hence, this study aims to compare the LSP performance of these SML and USML models, thus further to explore the advantages and disadvantages of these ML models and to realize a more accurate and reliable LSP result. Two representative SML models (support vector machine (SVM) and CHi-squared Automatic Interaction Detection (CHAID)) and two representative USML models (K-means and Kohonen models) are respectively used to scientifically predict the landslide susceptibility indexes, and then these prediction results are discussed. Ningdu County with 446 recorded landslides obtained through field investigations is introduced as case study. A total of 12 conditioning factors are obtained through procession of Landsat TM 8 images and high-resolution aerial images, topographical and hydrological spatial analysis of Digital Elevation Modeling in GIS software, and government reports. The area value under the curve of receiver operating features (AUC) is applied for evaluating the prediction accuracy of SML models, and the frequency ratio (FR) accuracy is then introduced to compare the remarkable prediction performance differences between SML and USML models. Overall, the receiver operation curve (ROC) results show that the AUC of the SVM is 0.892 and is slightly greater than the AUC of the CHAID model (0.872). The FR accuracy results show that the SVM model has the highest accuracy for LSP (77.80%), followed by the CHAID model (74.50%), the Kohonen model (72.8%) and the K-means model (69.7%), which indicates that the SML models can reach considerably better prediction capability than the USML models. It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reason for the low LSP accuracy of the USML models. Nevertheless, the USML models can also be used to implement LSP due to their advantages of efficient modeling processes, dimensionality reduction and strong scalability.

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Section: Sensitivity Analysis On Resolution Of Grid Unitsmentioning

confidence: 99%

Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

et al. 2020

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“…Its graph is based on True Positive Rate (TP/TP+FN), representing the probability of dividing an actual positive sample into a positive sample) is the vertical axis, and False Positive Rate (FP/FP+TN), representing the probability of dividing an actual negative sample into a positive sample) is one of the horizontal axis curve [49]. The value of AUC is between 0-1, the closer to 1, the better [50], if it is above 0.85, it is a better classifier.…”

Section: B Description Of Evaluation Indicatorsmentioning

confidence: 99%

Research on Disease Prediction Based on Improved DeepFM and IoMT

Amin

Alhussein

et al. 2021

IEEE Access

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In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM's hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT's sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.

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“…The inventory dataset include three types of landslide namely rockfall, shallow landslides, and debris flow. The inventory was split into two subsets (training (70%) and testing (30%)) ensuring each group has all the landslides types [27].…”

Section: B Inventory Datasetmentioning

confidence: 99%

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

Fanos

Pradhan

2019

IEEE Access

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Rockfall source identification is the most challenging task in rockfall hazard and risk assessment. This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow. The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas. High-resolution light detection and ranging data (LiDAR) was employed to derive the digital terrain model (DTM), from which several conditioning factors were extracted. These conditioning factors were optimized utilizing the ant colony optimization (ACO). Different machine learning algorithms, namely, logistic regression (LR), random tree (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN), in addition to their ensemble models (stacking, bagging, and voting), were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. On the other hand, the Gaussian mixture model (GMM) was optimized and applied for automatically identifying the slope threshold of the occurrence of the different landslide types. In order to reduce the model sensitivity to the alteration in various conditioning factors and to improve the model computations performance, land use probability area was formed. The rockfall sources were identified by integrating the probability maps and the reclassified slope raster based on the GMM results. The accuracy assessment reveals that the developed hybrid model can identify the probable rockfall regions with an accuracy of 0.95 based on the validation dataset and 0.94 on based the training dataset. The slope thresholds calculated by GMM were found to be > 58 • , 22 •-58 • , and 9 •-22 • for rockfall, shallow landslide, and debris flow, respectively. This indicates that the model can be generalized and replicated in different regions, and the proposed method can be applied in various landslides studies.

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Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

Cited by 53 publications

References 99 publications

Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Research on Disease Prediction Based on Improved DeepFM and IoMT

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

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