Research Projects

Modelling uncertainty in reservoir water quality 

Team: Lucy Marshall, Fiona Johnson, Maryam Zeinolabedini

In partnership with:  WaterNSW

This project provides a data-driven methodology to support cyanobacterial forecasting in WaterNSW storages and rivers and evaluates other data science opportunities for WaterNSW operations and research.

Mapping the impact of landscape dynamics on species diversity

Dare Team: Tristan Salles

Other members: Laurent Husson, Patrice Rey

Species distribution and richness ultimately result from complex interactions between biological, physical and environmental factors. We examine how changes in landscape morphology under different tectonic conditions and variable climatic forcing, might impact species distribution either by favouring migration routes or isolation. More specifically, we are looking at regional connectivity network using landscape evolution models that simulate drainage reorganisation and river captures and species movement using circuit theory and random walk algorithms. From these combined landscape evolution and connectivity models, we evaluate changes in biodiversification at regional scale and over Earth history.

Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

Team: Dr Richard Scalzo, Dr Mark Lindsay, Prof Mark Jessell, A/Prof Ed Cripps, Dr Guillaume Pirot, Dr Jeremie Giraud, Prof Sally Cripps

Geological modeling of subsurface structures is critical to decision-making across numerous application areas, including mining, groundwater, resource exploration, natural hazard assessment, and engineering, yet is also subject to considerable uncertainty. Blockworlds aids decision-making informed by models, and a novel step in the increasingly active area of research in geology and geophysics.

This paper has been published in Volume 15, Issue 9 of Geoscientific Model Development

Denison Summer Scholar

Team: Dr Nandini Ramesh & Morgan Kelly

The likelihood of atmospheric convection, and therefore heavy precipitation, over the tropical belt of the Earth is set by sea surface temperatures (SSTs). The physical theory explaining the relationship between SSTs and convection in the tropics does not, however, specify which properties of the distribution of SSTs most strongly influences the occurrence of tropical precipitation. While a few metrics have been proposed as approximations, this project sought to determine from data the most influential aspects of the SST distribution.

We first binned the distribution of tropical SSTs to obtain time series of the percentiles in increments of five as a set of twenty variables. We then used Lasso regression as a variable selection technique, regressing the convective area (based on cloud cover) on these twenty variables. We found that the best model based on information criteria was a function of the difference between the fifth and ninety-fifth percentiles, i.e., the width of the distribution, on seasonal timescales. On interannual timescales, we found that the thirty-fifth percentile of SST was the most influential part of the SST distribution. By spatially mapping this percentile, we deduced that this corresponds to the extent of the eastern boundary upwelling zones, highlighting the importance of ocean dynamics in setting the probability of tropical rainfall.

Quantum Algorithms for Variational Bayes and Monte Carlo Methods

Team: A/Prof Minh-Ngoc Tran and Dr Anna Lopatnikova

Quantum computing has emerged as the next computing technology paradigm, which promises to transform many critical fields, such as pharmaceutical and fertilizer design, supply chain and traffic optimization, or optimization for machine learning tasks.  Because quantum computers function fundamentally differently from classical computers, the emergence of quantum computing technology will lead to a new evolutionary branch of statistical and data analytics methodologies.

Our goal is to develop a suite of quantum statistical methods for the use in Bayesian machine learning.  For example, we have designed a quantum algorithm to speed up the computation of the natural gradient for VB.   Other directions include quantum-enhanced sequential Monte Carlo and quantum normalizing flows.

We also seek to enable the statistician and data scientist communities to collaborate with quantum algorithm designers and develop the next generation of methods.  To this end, we have written a primer on quantum computing for statisticians and data scientists, available at https://arxiv.org/abs/2112.06587.

Modelling the relationship between mining and water

Team: Prof Lucy Marshall, A/Prof Willem Vervoort, Prof Fiona Johnson,

Partnering with WaterNSW and NSW Smart Sensing Network (NSSN)

This project develops data science models to identify mining impacts, inform optimal sensor placement, and provide a guiding framework for best practice in water management in mined catchments.

Data Aided Stochastic Modelling of Cloud Brightening Plumes

Team: A/Prof Matt Cleary and Sam Davis

The objective of this research project is to develop, test and apply novel, probabilistic, data-centric models for the evolution of plumes of nano and micro scale seawater droplets and salt crystals to progress Cloud Brightening Technology to a level where a reduction in the incident radiation can be demonstrated at scale.

Partition-Mallows Model and Its Inference for Rank Aggregation

Team: Dr Wanchuang Zhu

Rank aggregation can obtain an aggregated ranking list based on multiple ranking lists. Its performance closely relies on equal reliabilities of all the rankers. The problem of discerning reliability of rankers based only on the rank data is of great interest to many practitioners, but has received less attention from researchers. This project aims to develop new methods which can distinguish the reliabilities of rankers. This project expects to simultaneously obtain a consensus ranking list and qualities of rankers.

Paper has been accepted by the Journal of the American Statistical Association.

Comparison of Catch-Only Data Limited Stock Assessment Models [Global Fishing Index, 2021]

Team: Dr Roman Marchant, Dr Vincent Chin, Dr Kendra Travaille

Partner: The Minderoo Foundation

There are numerous stock assessment methods specifically designed to deal with data-limited stocks with catch-only data. These models are applied in complex environments with large amounts of missing data in order to better understand the state of fish stocks and ultimately, guide management decisions. New and improved models are frequently proposed, including versions of CMSY, SSCOM, COMSIR and SRA+ among others. This project aims to provide a comprehensive and thorough comparison of catch-only stock assessment methods using three different simulation frameworks (FLR, DLMTool, and Rpath). These simulations generate a wide variety of ground truth data and removes bias in the evaluations arising from the selection of a single simulation technique. By using diverse simulation frameworks, we aim to better quantify and characterise the accuracy of each model as a function of stock characteristics, such as fish life histories, data availability and effort dynamics.

Probabilistic Geophysical Inversions using Forward-Modeled Geology

Team: Professor Mark Jessell, Dr Mark Lindsay, Dr Richard Scalzo

Inference of subsurface geological structure is a key part of decision-making in a range of contexts, including mineral exploration, environmental impacts from mining operations, natural hazard assessment, and engineering.  Direct probes of geology from borehole logs are sparse and expensive, and must be fused with sensor data and geological prior knowledge to yield an uncertain model of the subsurface.  While both geophysical imaging and geological modeling are mature areas, uncertainty-quantified inference of geological parameters or histories from geophysics is still uncommon, and poses interesting methodological challenges.

This research stream focuses on parametric representations of geology, including “implicit” and “kinematic” models that embody simplified descriptions of geological processes, and how to embed them within Bayesian statistical models for uncertainty-quantified inference.  Like many inverse problems, the posterior for the resulting model often has complex geometry, with strong correlations, curvature, and multiple modes.  Research focuses on how to quickly and efficiently sample these posteriors, and particularly on approximate methods, such as emulators and surrogates, to accelerate sampling.

Papers have been accepted by: Earth System Science Data   Geoscientific Model Development 2021 Geoscientific Model Development 2019.

Remote Sensing Solutions for Water Accounting

DARE Team: Associate Professor Willem Vervoort, Dr Richard Scalzo, Dr Ignacio Fuentes

DARE Partners: Alan Turing Institute, WaterSense

Remote sensing time series holds great promise for data-driven management of water resources at catchment scale.  This research stream develops new uncertainty-enabled water accounting methodologies, fusing data from the microwave-band Sentinel satellite with in-situ measurements.  Techniques used include probabilistic modeling with physical components, Gaussian process mixtures, and deep learning.

An initial paper using Sentinel data to estimate the time-varying capacity of lakes and on-farm reservoirs has been published in ScienceDirect.