Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms

Abstract

A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information.

Advanced data mining techniques for landslide susceptibility mapping

“…Conducting a performance analysis on results obtained through data mining techniques is crucial and cannot be over-emphasized (Remondo et al 2003;Brock et al 2020;Mohammadifar et al 2021). In addition, the evaluations help verify and define the level of accuracy and performance of the landslides susceptibility models (Althuwaynee et al 2021). Results from the performance evaluation show that the AUROC value for SVM on the training datasets is 0.833 against 0.814 and 0.792 for both RT and NBM.…”

“…This study has assessed the effectiveness of advanced data mining techniques to evaluate landslides in Lawas, an economic giant town in Sarawak, Malaysia. In achieving the said objectives of the study, three machine learning algorithms, namely the SVM, RT, and NBM, were used to train geospatial data extracted from various GIS sources (Bacha et al 2020;Althuwaynee et al 2021). The training was made to develop classification between identified landslides location in the area to non-landslides locations by examining the pixels of two classes.…”

“…While from 2004 to the year 2016, China alone has spent over one billion dollars on non-seismic landslides. Overall, the figures relating to landslide losses have been rising despite governments' and individuals' efforts to curtail the phenomenon (Remondo et al 2003;Petley 2012;Pourghasemi and Rahmati 2018;Althuwaynee et al 2021). Due to the dynamic nature of landslides and their analysis, there have been improvements in studies that analyze landslides and provide early warnings of their occurrence in susceptible regions.…”

An Experiment to Design an Operation and Maintenance System Integrating Apriori Association Rules for a Telecom Platform

“…An enterprise financial risk analysis model based on interactive mining of association rules from financial indicators is proposed by Lin and Chen [30]. In the field of disaster monitoring, based on digital aerial images and the landslide caused by heavy rainfall event in the southwest of South Korea in 1998, the uncertainty of unlabelled features in the landslide is accomplished by t-SNE clustering and Apriori algorithms, and the results can provide the reference for the classification of missing or outdated spatial attribute information [31]. The correlation among tax data is mined by association rules and then established the identification path of tax evasion [32].…”

The Use of the Principal Component Analysis for the Prediction of Superlattices Hard Perovskites and Inverse Perovskites

“…It is extensively applied in image processing, NLP, genomic data and speech processing [41]. Recently Park et al [42], has used the t-SNE method in order to explore the formability of mixed-ion perovskites. Their results are promising and demonstrate that t-SNE is a robust nonlinear technique to reduce the dimensionality of the input variables space.…”