Jinran Wu

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• Doctor of Philosophy
• PostDoc Position at The University of Queensland

Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities. Daily groundwater depth (GWD) data from 43 wells (2018–2022) were collected in three coastal cities in Jiangsu Province, China. Seasonal and Trend decomposition using Loess (STL) together with wavelet analysi...

Physics-Informed Neural Networks (PINNs) have gained significant attention for their simplicity and flexibility in engineering and scientific computing. In this study, we introduce a normalized PINN (NPINN) framework to solve a class of wave propagation equations in non-unitized domains over extended time ranges. This is achieved through a normaliz...

A rater's ability to assign accurate scores can significantly impact the outcomes of educational assessments. However, common indices for evaluating rater characteristics typically focus on either their severity or their discrimination ability (i.e., skills to differentiate between students). Additionally, these indices are often developed without...

In financial markets, accurate stock price movement prediction can significantly enhance investors' profits. However, the stock price is a highly complex dynamic system with considerable fluctuations, and the accuracy of direction prediction can be improved by selecting appropriate technical indicators. In this work, we propose a novel sparse suppo...

Introduction Due to its favorable traits-such as lower lignin content, higher oil concentration, and increased protein levels-the genetic improvement of yellow-seeded rapeseed has attracted more attention than other rapeseed color variations. Traditionally, yellow-seeded rapeseed has been identified visually, but the complex variability in the seed...

Study region: Poyang Lake, China's largest freshwater lake Study focus: The water level variations of Poyang Lake and the combined effects of the upstream rivers and the Yangtze River during extreme drought events are not yet fully understood. In this study, the temporal and spatial variations of Poyang Lake's water level and the river-lake interac...

This chapter uses predicting student certification in online courses hosted on edX to demonstrate how to apply three powerful regularization methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP) in constructing a predictive model. These advanced statist...

Modern automated generation control (AGC) is increasingly complex, requiring precise frequency control for stability and operational accuracy. Traditional PID controller optimization methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios. This paper introduces an enhanced strategy using...

Prediction methods have garnered significant attention in intelligent decision-making. Most existing approaches to predicting crude oil prices prioritize accuracy and stability while providing precise prediction intervals (PIs) that can offer valuable insights. Thus far, we introduce a novel hybrid model to forecast future crude oil prices. Our app...

To address the sensitivity of parameter and dissatisfactory precision for physics informed extreme machine learning (called PIELM) with common sigmoid, tangent and gaussian activation functions in solving high order partial differential equations (PDEs) arised from the fields of scientific computation and engineering applications. In this work, a F...

Accurate short-term power forecasting (STPF) provides reliable support for the stable operation of power systems. However, due to the randomness of consumer behavior and energy properties, outliers inevitably exist in power series. Considering its negative influence, effectively extracting features from the power series with outliers has become a s...

Long Short-Term Memory (LSTM) is widely used in time series prediction, however, few people have noticed its predictions always lag behind corresponding measurements, which greatly reduces the predictive and warning capabilities of LSTM. The present paper proposes a new hybrid model that couples empirical/variational mode decomposition (E/VMD) and...

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maxi...

Empirical models and process-based theoretical or numerical models are valuable tools for analyzing sediment dynamics by reproducing the SSC observations, but they are either limited by simplified assumptions or computationally complex. Some data-driven models have been developed to predict SSC but are still weak in explaining the causes of its cha...

The COVID-19 pandemic led to significant disruptions in schooling worldwide. This study aims to evaluate and compare the impact of the pandemic on student mathematics achievement across eight representative countries/regions, using data from the Programme for International Student Assessment (PISA) 2022. The multilevel random forest (RF) method was...

This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading sol...

Lake temperature forecasting is crucial for understanding and mitigating climate change impacts on aquatic ecosystems. The meteorological time series data and their relationship have a high degree of complexity and uncertainty, making it difficult to predict lake temperatures. In this study, we propose a novel approach, Probabilistic Quantile Multi...

Bayesian Physics Informed Neural Networks (BPINN) have received considerable attention for inferring differential equations' system states and physical parameters according to noisy observations. However, in practice, Hamiltonian Monte Carlo (HMC) used to estimate the internal parameters of BPINN often encounters troubles, including poor performanc...

Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging themes, this study analyzed 876 articles published between 2015 and 2022, retrieved from the Web of Science database. Leveraging CiteSpace visualiz...

Accurate short-term load forecasting (STLF) is crucial for the power system. Traditional methods generally used signal decomposition techniques for feature extraction. However, these methods are limited in extrapolation performance, and the parameter of decomposition modes needs to be preset. To end this, this paper develops a novel STLF algorithm...

Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assignin...

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could...

A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory vari...

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a s...

Multidimensional forced-choice (MFC) items have been found to be useful to reduce response biases in personality assessments. However, conventional scoring methods for the MFC items result in ipsative data, hindering the wider applications of the MFC format. In the last decade, a number of item response theory (IRT) models have been developed, majo...

The transmission of inflation is a widespread occurrence, and managing inflationary pressures is a crucial macroeconomic challenge. Although inflation is a typical macroeconomic variable, its contemporaneous and lagged causal relationships have not been thoroughly investigated, which could result in missing important policy insights. The Bayesian g...

Power systems are pivotal in providing sustainable energy across various sectors. However, optimizing their performance to meet modern demands remains a significant challenge. This paper introduces an innovative strategy to improve the optimization of PID controllers within nonlinear oscillatory Automatic Generation Control (AGC) systems, essential...

Wind power forecasting techniques have been well developed over the last half-century. There has been a large number of research literature as well as review analyses. Over the past 5 decades, considerable advancements have been achieved in wind power forecasting. A large body of research literature has been produced, including review articles that...

Internal solitary wave (ISW), as a typical marine dynamic process in the deep sea, widely exists in oceans and marginal seas worldwide. The interaction between ISW and the seafloor mainly occurs in the bottom boundary layer. For the seabed boundary layer of the deep sea, ISW is the most important dynamic process. This study analyzed the current sta...

Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditi...

Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe into solving a class of elliptic partial differential equations (PDEs) with multiple scales by utilizing Fourier-based mixed physics informed neural networks (dubbed FMPINN)...

In‐situ observations of hydrodynamics and suspended sediment concentrations (SSCs) were conducted on an abandoned lobe in the northern part of the modern Yellow River Delta, China. The SSC record at the site is found to be the superposition of a general trend (fast increase and slow decrease cycle) caused by storm waves (SubSSC1) and relatively sma...

The interest in predicting online learning performance using ML algorithms has been steadily increasing. We first conducted a scientometric analysis to provide a systematic review of research in this area. The findings show that most existing studies apply the ML methods without considering learning behavior patterns, which may compromise the predi...

The Support Vector Regression (SVR) technique can approximate intricate systems by addressing learning and estimation challenges within a reproducing kernel Hilbert space, devoid of reliance on specific parameter assumptions. However, when dealing with correlated data like time series, the SVR method often falls short in accounting for underlying t...

Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is physics-informed neural networks (PINN), which integrate physical principles into neural networks. This transforms the process of solving PDEs into optimization problems for neural networks. To...

Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is on physics-informed neural networks (PINNs), which integrate physical principles into neural networks. This transforms the process of solving PDEs into optimization problems for neural networks....

Analytical solutions are practical tools in ocean engineering, but their derivation is often constrained by the complexities of the real world. This underscores the necessity for alternative approaches. In this study, the potential of Physics-Informed Neural Networks (PINN) for solving the one-dimensional vertical suspended sediment mixing (settlin...

This work aims to provide a review of methodology on analysis of longitudinal data focusing on (i) how to select different model components: the covariance (correlation and variance) functions or structures and the predictive variables; (ii) the robust approaches including rank and quantile regression; and (iii) machine learning algorithms that inc...

Physics-informed neural networks (PINNs) have been widely utilized for solving a range of partial differential equations (PDEs) in various scientific and engineering disciplines. This paper presents a Fourier heuristic enhanced PINN (termed FCPINN) designed to address a specific class of biharmonic equations with Dirichlet and Navier boundary condi...

We propose an adjusted robust heteroscedastic autoregressive spatiotemporal model with a data-driven process to predict the hourly PM 2:5 concentrations in Xi'an and Xianyang, China. To begin with, an adjusted variance function for the heteroscedastic model is proposed to capture the different variances of the PM2.5 concentrations during the period...

With the increasing number of sequenced species, phylogenetic profiling (PP) has become a powerful method to predict functional genes based on co-evolutionary information. However, its potential in plant genomics has not yet been fully explored. In this context, we combined the power of machine learning and PP to identify salt stress-related genes...

Electricity demand forecasting is crucial for practical power system management. However, during the COVID-19 pandemic, the electricity demand system deviated from normal system, which has detrimental bias effect in future forecasts. To overcome this problem, we propose a deep learning framework with a COVID-19 adjustment for electricity demand for...

Electricity demand forecasting is of great significance to the electricity system and residents' life, but it is difficult to forecast the electricity demand series because of the influence of cyclical factors. Electricity demand forecasting also faces the problem of small data amounts. Therefore, we need to design a model that is less affected by...

Accurately predicting runoff (Q) and Suspended Sediment Concentration (SSC) is crucial for the environmental and geological evolution of the Yellow River Delta, a region with numerous oil fields and wetlands. However, accurate prediction of Q and SSC in the Yellow River Delta, characterized by the intricate interplay of high-frequency and low-frequ...

Forecasting stock market movements is a challenging task from the practitioners’ point of view. We explore how model selection via the least absolute shrinkage and selection operator (LASSO) approach can be better used to forecast stock closing prices using real-world datasets of daily stock closing prices of three major international airlines. Com...

The utilization of gene selection techniques is crucial when dealing with extensive datasets containing limited cases and numerous genes, as they enhance the learning processes and improve overall outcomes. In this research, we introduce a hybrid method that combines the binary reptile search algorithm (BRSA) with the LASSO regression method to eff...

The extreme learning machine (ELM) is a well-known approach for training single hidden layer feedforward neural networks (SLFNs) in machine learning. However, ELM is most effective when used for regression on datasets with simple Gaussian distributed error because it often employs a squared loss in its objective function. In contrast, real-world da...

Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The...

The transmission of inflation is a widespread occurrence, and managing inflationary pres-sures is a crucial macroeconomic challenge. Although inflation is a typical macroeconomic variable, its contemporaneous and lagged causal relationships have not been thoroughly investigated, which could result in missing important policy insights. The Bayesian...

Deep neural networks have received significant attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this work, we probe into solving a class of elliptic PDEs with multiple scales by means of Fourier-based mixed physics-informed neural networks (called FMPINN), and its solver is configured as...

Optimization problems are ubiquitous in engineering and scientific research, with a large number of such problems requiring resolution. Meta-heuristics offer a promising approach to solving optimization problems. The firefly algorithm (FA) is a swarm intelligence meta-heuristic that emulates the flickering patterns and behaviour of fireflies. Altho...

In time series forecasting with outliers and random noise, parameter estimation in a neural network via minimizing the $l_{2}$ loss is unreliable. Therefore, an adaptive rescaled lncosh loss function is proposed in this article to handle time series modeling with outliers and random noise. It overcomes the limitation of the single distribution of...

An event-triggered output feedback control approach is proposed via a disturbance observer and adaptive dynamic programming (ADP). The solution starts from constructing a nonlinear disturbance observer which only depends on the measurement of system output. A state observer is then developed based on approximation information of system dynamics via...

The COVID-19 pandemic has given rise to significant changes in electricity demand around the world. Although these changes differ from region to region, countries that have implemented stringent lockdown measures to curtail the spread of the virus have experienced the greatest alterations in demand. Within Australia, the state of Victoria has been...

As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sen...

Fault-tolerant control (FTC) of a non-affine system with uncertainties is one of critical issues in nonlinear control. In this paper, in the presence of unknown actuator failures, an event-trigger-based FTC scheme is proposed for such a nonlinear system with predefined performance. For actuator failures, a compensation mechanism is designed to alle...

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate s...

The hyperparameters in support vector regression (SVR) determine the effectiveness of the support vectors with fitting and predictions. However, the choice of these hyperparameters has always been challenging in both theory and practice. The ν-support vector regression eliminates the need to specify an value elegantly, but at the cost of specifying...

The presence of heterogeneous variances is the norm in practice, which makes machine learning predictions less reliable when noise variance is implicitly assumed to be equal. To this end, we extend support vector regression by allowing a range of variances in the model training. Specifically, we model the variance as a function of the mean and othe...

Standard methods for forecasting electricity loads are not robust to cyberattacks on electricity demand data, potentially leading to severe consequences such as major economic loss or a system blackout. Methods are required that can handle forecasting under these conditions and detect outliers that would otherwise go unnoticed. The key challenge is...

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristi...

Deep learning—in particular, deep neural networks (DNNs)—as a mesh-free and self-adapting method has demonstrated its great potential in the field of scientific computation. In this work, inspired by the Deep Ritz method proposed by Weinan E et al. to solve a class of variational problems that generally stem from partial differential equations, we...

Centrality has always been used in transportation networks to estimate the status and importance of a node in the networks, especially in the shipping networks. However, most of the studies only take the shipping network as an unweighted network or only considering the tie weights in the weighted networks, ignoring the truth that both the number of...

In load forecasting fields, electricity demand with hierarchical structure is very popular where there are some differences among investigated load series because of geography or customers' habits. Common methods usually ignore their differences and introduce some complex models to improve forecasting performance. Therefore, appropriately dealing w...

Recently, the interactions between internal solitary waves (ISWs) and the seabed have directed increasing attention to ocean engineering and offshore energy. In particular, ISWs induce bottom currents and pressure fluctuations in deep water. In this paper, we propose a method for predicting the shear stress induced by shoaling ISWs based on machine...

The insensitivity parameter in support vector regression determines the set of support vectors that greatly impacts the prediction. A data-driven approach is proposed to determine an approximate value for this insensitivity parameter by minimizing a generalized loss function originating from the likelihood principle. This data-driven support vector...

Accurate power load forecasting plays an integral role in power systems. To achieve high prediction accuracy, models need to extract effective features from raw data, and the training of models needs a large amount of data. However, data sharing will require the disclosure of the private data of the participants. To address this issue, we combined...

Accurate air quality index (AQI) forecasting makes a difference to public health, local economic development, and ecological environment. As a typical geographical datum, the spatial autocorrelation (SAC) of the AQI is often ignored, which may violate the assumptions of some models, such as machine learning which requires variables to be independen...

This thesis combines support vector machines with statistical models for analyzing data generated by complex processes. The key contribution of the thesis is to propose five regression frameworks aiming for hyperparameter estimation, support vector selection, data modelling with unequal variances, temporal patterns, and cost benefit analysis. A new...

Accurate power load forecasting has a significant effect on a smart grid by ensuring effective supply and dispatching of power. However, electric load data generally possesses the characteristics of nonlinearity, periodicity, and seasonality. In particular, for complex electric load systems, the presence of redundant information potentially reduces...

In situ observations of suspended sediment concentrations (SSCs), bed elevations, and concurrent hydrodynamics were conducted with an instrumented tripod on an abandoned lobe of the Yellow River Delta in the winter of 2014–2015. Four typical winter storms were recorded, but no significant local seabed erosion was observed at the observation site. N...

Equilibrium scour depth (S) of seabed is critical to the safety of offshore pipelines which is one of the most important topics in ocean engineering. Compared to sands, few experiments have been done for silty seabed. In the present work, scour experiments under wave-only action were performed for both sandy and silty seabeds. Together with the dat...