Application of artificial intelligence in geotechnical and geohazard investigations

Zhang, Wengang; Pradhan, Biswajeet; Stuyts, Bruno; Xu, Chong

doi:10.1002/gj.4779

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…Moreover, these technologies represent an opportunity to bridge quantitative and qualitative approaches in physical geography, human geography, and Geographical Information Science (Ferreira and Vale, 2022). AI has been instrumental in risk estimation and susceptibility assessment related to landslides and other geohazards (Zhang et al, 2023) and for tackling non-linear hydrological problems such as streamflow and rainfall prediction and aspects of the cryosphere (Sun et al, 2022). AI also enables the development of sophisticated models that simulate complex environmental processes, such as climate change, land use change, and natural hazards, ultimately supporting the optimal prediction and visualisation of future landscape states, environmental change scenarios, and effective mitigation strategies.…”

Section: Novel Epistemological Framework For Physical Geographymentioning

confidence: 99%

A horizon scan for novel and impactful areas of physical geography research in 2023 and beyond

Anderson,

Tooth,

Kim

et al. 2023

Progress in Physical Geography: Earth and Environment

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This editorial reports on a horizon scan exercise that was undertaken to identify new frontier topics, new or emerging themes/concepts, or new philosophical questions of relevance to physical geography. Researchers with broad geographical and disciplinary scope, all of whom were members of the journal's editorial board or editorial advisory board, were invited to join a horizon scan panel. The horizon scan Chair canvassed panel members for ideas, resulting in an initial 32 independently proposed topics. Similar topics were merged by the Chair, and panel members were then invited to vote on and score anonymously the remaining 28 topics, bearing in mind the perceived importance/relevance and novelty for the discipline. The final ranking and sifting phase produced a list of 12 topics, which were categorised as being either new study areas or new epistemological frameworks for physical geography. In this editorial, we outline these 12 topics, some of which have been inspired by developments outside of the discipline, but we identify how potentially fertile contributions could be added by physical geographers. We discuss how new studies of extreme event geographies, the impacts of compound stressors, cross-system pollution and toxicity, the geomorphological basis of zoonoses, ancient environmental DNA, the projection and visualisation of landscape futures, and planetary sciences can all benefit from additional physical geography perspectives. We then consider the ways in which physical geographers may engage further with new approaches in personalised and internet-of-things monitoring, artificial intelligence, innovative technologies for teaching physical geography, the study of human-climate impacts, and the raising of the profile of physical geography thinking alongside other knowledge forms. We encourage more physical geographers to apply their unique skillsets and ways of thinking to these topics. The journal will welcome new submissions, or proposals for special issues, that address these topics from physical geographers and their colleagues.

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Section: Novel Epistemological Framework For Physical Geographymentioning

confidence: 99%

A horizon scan for novel and impactful areas of physical geography research in 2023 and beyond

Anderson,

Tooth,

Kim

et al. 2023

Progress in Physical Geography: Earth and Environment

View full text Add to dashboard Cite

show abstract

“…• Importance of explainable AI (XAI): While traditional ML models have been criticized for their opacity, the emerging field of XAI seeks to bridge this gap. Techniques within XAI aim to provide clarity on how decisions are made within an algorithm, ensuring that professionals can trust and act on these insights [65].…”

Section: Post-model Analysis and Interpretationmentioning

confidence: 99%

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Akbar Firoozi,

Asghar Firoozi

2023

Artificial Intelligence

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Within the domain of geotechnical engineering, risk assessment is pivotal, acting as the linchpin for the safety, durability, and resilience of infrastructure projects. While traditional methodologies are robust, they frequently require extensive manual efforts and can prove laborious. With the onset of the digital era, machine learning (ML) introduces a paradigm shift in geotechnical risk assessment. This chapter delves into the confluence of ML and geotechnical engineering, spotlighting its enhanced predictive capabilities regarding soil behaviors, landslides, and structural resilience. Harnessing modern datasets and rich case studies, we offer an exhaustive examination that highlights the transformative role of ML in reshaping geotechnical risk assessment practices. Throughout our exploration of evolution, challenges, and future horizons, this chapter emphasizes the significance of ML in advancing and transforming geotechnical practices.

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“…It prioritizes the conditioning factors based on specific criteria. In recent years, various types of efficient machine learning algorithms have emerged [23][24][25] and have been increasingly used in avalanche research. Machine-learning-based avalanche susceptibility analysis entails inputting a set of avalanche occurrence locations and conditioning factor data into various supervised learning models to train complex nonlinear relationships between avalanche disasters and their conditioning factors.…”

Section: Introductionmentioning

confidence: 99%

Avalanche Susceptibility Mapping by Investigating Spatiotemporal Characteristics of Snow Cover Based on Remote Sensing Imagery along the Pemo Highway—A Critical Transportation Road in Tibet, China

Xi,

Mei

2023

Water

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The Pemo Highway is a critical transportation road to Medog County in the Tibet Plateau (TP). Since its completion in 2021, the Pemo Highway has been prone to frequent avalanches due to heavy rainfall and snowfall. Despite the lack of monitoring stations along the highway and limited research conducted in this area, remote sensing imagery provides valuable data for investigating avalanche hazards along the highway. In this paper, we first investigated the spatiotemporal characteristics of snow cover along the Pemo Highway over the past two years based on the GEE platform. Second, we integrated snow, topography, meteorology, and vegetation factors to assess avalanche susceptibility in January, February, and March 2023 along the highway using the AHP method. The results reveal that the exit of the Duoshungla Tunnel is particularly susceptible to avalanches during the winter months, specifically from January to March, with a significant risk observed in March. Approximately 3.7 km in the direction of the tunnel exit to Lager is prone to avalanche hazards during this period. The recent “1.17 avalanche” event along the Pemo Highway validates the accuracy of our analysis. The findings of this paper provide timely guidance for implementing effective avalanche prevention measures on the Pemo Highway.

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Application of artificial intelligence in geotechnical and geohazard investigations

Cited by 11 publications

References 31 publications

A horizon scan for novel and impactful areas of physical geography research in 2023 and beyond

A horizon scan for novel and impactful areas of physical geography research in 2023 and beyond

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Avalanche Susceptibility Mapping by Investigating Spatiotemporal Characteristics of Snow Cover Based on Remote Sensing Imagery along the Pemo Highway—A Critical Transportation Road in Tibet, China

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