scholarly journals Machine Learning: A Novel Approach to Predicting Slope Instabilities

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Upasna Chandarana Kothari ◽  
Moe Momayez
Keyword(s):  
Machine Learning ◽  
Slope Failure ◽  
Failure Time ◽  
Working Environment ◽  
Slope Failures ◽  
Active Monitoring ◽  
Novel Approach ◽  
Geomechanical Analysis ◽  
Failure Predictions ◽  
Slope Instabilities

Geomechanical analysis plays a major role in providing a safe working environment in an active mine. Geomechanical analysis includes but is not limited to providing active monitoring of pit walls and predicting slope failures. During the analysis of a slope failure, it is essential to provide a safe prediction, that is, a predicted time of failure prior to the actual failure. Modern-day monitoring technology is a powerful tool used to obtain the time and deformation data used to predict the time of slope failure. This research aims to demonstrate the use of machine learning (ML) to predict the time of slope failures. Twenty-two datasets of past failures collected from radar monitoring systems were utilized in this study. A two-layer feed-forward prediction network was used to make multistep predictions into the future. The results show an 86% improvement in the predicted values compared to the inverse velocity (IV) method. Eighty-two percent of the failure predictions made using ML method fell in the safe zone. While 18% of the predictions were in the unsafe zone, all the unsafe predictions were within five minutes of the actual failure time, all practical purposes making the entire set of predictions safe and reliable.

scholarly journals On the influence of topographic, geological and cryospheric factors on rock avalanches and rockfalls in high-mountain areas

2012 ◽  
Vol 12 (1) ◽  
pp. 241-254 ◽  
Author(s):  
L. Fischer ◽  
R. S. Purves ◽  
C. Huggel ◽  
J. Noetzli ◽  
W. Haeberli
Keyword(s):  
Slope Failure ◽  
Temporal Distribution ◽  
High Mountain ◽  
European Alps ◽  
Permafrost Degradation ◽  
Slope Failures ◽  
Mountain Areas ◽  
Investigation Area ◽  
Rock Avalanches ◽  
Slope Instabilities

Abstract. The ongoing debate about the effects of changes in the high-mountain cryosphere on rockfalls and rock avalanches suggests a need for more knowledge about characteristics and distribution of recent rock-slope instabilities. This paper investigates 56 sites with slope failures between 1900 and 2007 in the central European Alps with respect to their geological and topographical settings and zones of possible permafrost degradation and glacial recession. Analyses of the temporal distribution show an increase in frequency within the last decades. A large proportion of the slope failures (60%) originated from a relatively small area above 3000 m a.s.l. (i.e. 10% of the entire investigation area). This increased proportion of detachment zones above 3000 m a.s.l. is postulated to be a result of a combination of factors, namely a larger proportion of high slope angles, high periglacial weathering due to recent glacier retreat (almost half of the slope failures having occurred in areas with recent deglaciation), and widespread permafrost occurrence. The lithological setting appears to influence volume rather than frequency of a slope failure. However, our analyses show that not only the changes in cryosphere, but also other factors which remain constant over long periods play an important role in slope failures.

scholarly journals Determining the Geotechnical Slope Failure Factors via Ensemble and Individual Machine Learning Techniques: A Case Study in Mandi, India

2021 ◽  
Vol 9 ◽  
Author(s):  
Naresh Mali ◽  
Varun Dutt ◽  
K. V. Uday
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Slope Failure ◽  
Feature Selection Method ◽  
Failure Prediction ◽  
Selection Method ◽  
Support Vector ◽  
Selection Methods ◽  
Slope Failures ◽  
Causal Factors

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

2021 ◽  
Author(s):  
Brij B. Gupta ◽  
Krishna Yadav ◽  
Imran Razzak ◽  
Konstantinos Psannis ◽  
Arcangelo Castiglione ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Novel Approach

scholarly journals Functional prediction of environmental variables using metabolic networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adèle Weber Zendrera ◽  
Nataliya Sokolovska ◽  
Hédi A. Soula
Keyword(s):  
Machine Learning ◽  
Growth Temperature ◽  
Environmental Variables ◽  
Metabolic Networks ◽  
Machine Learning Techniques ◽  
Underlying Structure ◽  
Glutathione Biosynthesis ◽  
Additional Information ◽  
Cold Environments ◽  
Novel Approach

AbstractIn this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

scholarly journals A New Model Averaging Approach in Predicting Credit Risk Default

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 114
Author(s):  
Paritosh Navinchandra Jha ◽  
Marco Cucculelli
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Model Averaging ◽  
Research Direction ◽  
Future Research ◽  
Classification Problems ◽  
Average Technique ◽  
Model Average ◽  
Novel Approach ◽  
Unbalanced Dataset

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

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