Project Overview

In 2019, the world witnessed one of the most catastrophic dam collapses that wreaked havoc on the Amazonian metropolis of Belo-Horizonte, through a fatal combination of debris-flow and flooding. It led to tragic loss of life, property and livestock. Sadly, as drastic changes to climate continue so do rates and severity of events such as landslides. In this interdisciplinary project, a collaboration between two leading Australian universities - Curtin University and the University of Western Australia - we combine expertise of Geophysics, Applied Mathematics, Geotechnical Engineering, and Statistics to investigate the causal mechanisms underpinning various landslide phenomena. Some specific objectives are given below.

Aims

We have shown that advanced statistical and physical methods are able to model and predict uncertainty in spatial-temporal displacement data with reasonable confidence. A pilot study has indicated that precursors to apparent landslide failures may be detected up to 6 months in advance (Das et al 2024) using statistical analysis of displacement time series. This proposed project will introduce and extend statistical approaches to monitoring spatial and temporal variations of landslides.  
A key aim of this project would be to allow end-users: first responders, policy makers and communities enough lead time to plan for disaster mitigation plans.

Objectives

This project will make the following contributions to landslide literature: 
1. Propose and investigate statistical methodologies for temporal change point detection, spatial point pattern methodology and various geostatistical methods including spatial generalized linear models. 
2.  Interpret the suitability of the statistical methodologies against physics-based soil mechanics framework that govern the slope failure mechanism for both ductile and brittle soils. 
3. Consider the scalability of the investigated methods against various types of landslides and patterns of failure that can include rockfall, debris flow. 
4. Compare these statistical approaches with conventional state-of-the-art deterministic power-law based strategies that have origins in failure law in material science.

Significance 

Landslides are a major natural hazard that are often entwined and triggered by other hazards such as rainfall, floods, and earthquakes. Burgeoning population in a climate change regime have often driven human activity and settlements in geographical regions that have unstable slopes, exposing communities to the risk of landslides. This has led to an increase in landslides related disasters around the globe. But less industrialized nations are disproportionately affected by all natural hazards. The outcomes of this research will directly contribute to disaster preparedness and mitigation.

This interdisciplinary project leverages the expertise of a renowned team at Curtin University (Dr. Sourav Das, Prof. Klaus Regenauer-Lieb) and the University of Western Australia (Dr. Cristina Vulpe, Dr. Gopalan Nair) in Geophysics, Applied Mathematics, Geotechnical Engineering, and Statistics. Collaborations with leading researchers and access to the combined resources of both universities ensure the project's feasibility. 
Project Significance: Landslides are a growing threat due to climate change and human encroachment on unstable land. This project aims to develop a revolutionary landslide prediction system using advanced statistical methods. Building upon a pilot study demonstrating the potential for 6-month pre-failure detection, the project joins a novel physics-based forward modelling approach with an advanced statistically based data-assimilation method to develop a novel data-driven landslide forecasting method. 
Impact: 

This research holds significant societal impact. By providing early landslide warnings, the project has the potential to save lives and infrastructure. This is particularly crucial for developing nations disproportionately affected by natural disasters. The developed statistical models will be valuable tools for disaster mitigation planning by providing crucial lead time to first responders and communities.

• Future Students

• Faculty of Science & Engineering
  • Science courses
  • Engineering courses

• Higher Degree by Research

• Australian Citizen
• Australian Permanent Resident
• New Zealand Citizen
• Permanent Humanitarian Visa
• International Student

• Merit Based

The annual scholarship package, covering both stipend and tuition fees, amounts to approximately $70,000 per year.

In 2024, the RTP stipend scholarship offers $35,000 per annum for a duration of up to three years. Exceptional progress and adherence to timelines may qualify students for a six-month completion scholarship.

Selection for these scholarships involves a competitive process, with shortlisted applicants notified of outcomes by November 2024.

1

All applicable HDR courses.

Candidates with strong quantitative skills, including familiarity with statistical inference, multivariate statistics or machine-learning methods. Proficiency in one or more the programming languages R or Python, are desired for this project. Must be eligible to enrol in PhD programs at Curtin.

 

Please send your CV, academic transcripts and brief rationale why you want to join this research project via the HDR Expression of Interest form to the project lead researcher, listed below. 

You must be enrolled in a Higher Degree by Research Course at Curtin University by March 2025.

Project Lead: Dr Sourav Das