K.W. Chau | Hong Kong Polytechnic University (original) (raw)

Books by K.W. Chau

Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to... more Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. This book, being also a Special Issue of the journal Water, is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in rainfall-runoff modeling. The information and analyses are intended to contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures. Being the editor of this book, I would like to thank all authors contributing to the fourteen chapters as well as the reviewers involved and who have provided constructive comments on these articles during the reviewing process.

This book reviews the state-of-the-art in conventional coastal modelling as well as in the increa... more This book reviews the state-of-the-art in conventional coastal modelling as well as in the increasingly popular integration of various artificial intelligence technologies into coastal modelling. Conventional hydrodynamic and water quality modelling techniques comprise finite difference and finite element methods. The novel algorithms and methods include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems.

This book makes an endeavor to improve the accuracy of hydrological forecasting in three aspects,... more This book makes an endeavor to improve the accuracy of hydrological forecasting in three aspects, model inputs, selection of models, and data-preprocessing techniques. Seven input techniques, namely, linear correlation analysis (LCA), false nearest neighbors, correlation integral, stepwise linear regression, average mutual information, partial mutual information, artificial neural network (ANN) based on multi-objective genetic algorithm, are first examined to select optimal model inputs in each prediction scenario. Representative models, such as K-nearest-neighbors (K-NN) model, dynamic system based model (DSBM), ANN, modular ANN (MANN), and hybrid artificial neural network-support vector regression (ANN-SVR), are then proposed to conduct rainfall and streamflow forecasts. Four data-preprocessing methods including moving average (MA), principal component analysis (PCA), singular spectrum analysis (SSA), and wavelet analysis (WA), are further investigated by integration with the abovementioned forecasting models.

Liquid retaining structures are more vulnerable to corrosion problems and thus have stringent req... more Liquid retaining structures are more vulnerable to corrosion problems and thus have stringent requirements against serviceability limit state of crack. The design procedures of these structures require significant empirical inputs from specialists. With the recent advent of artificial intelligence technology, a coupled knowledge-based system can handle both the symbolic knowledge processing based on engineering heuristics in the preliminary synthesis stage and the extensive numerical crunching involved in the detailed structural analysis stage. This new book presents a prototype coupled knowledge-based system for the design of liquid retaining structures. (Imprint: Nova Press)

Papers by K.W. Chau

Expert Systems With Applications, 2024

The important foundation for water resource management and utilization is effective monthly runof... more The important foundation for water resource management and utilization is effective monthly runoff prediction. In this study, a new coupled model for predicting monthly runoff is proposed. In order to predict the decomposed subsequences separately using an ELMAN neural network optimized by the sparrow search algorithm (SSA), this model first decomposes the original runoff series using robust local mean decomposition (RLMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) predicts the decomposed subsequences separately using an ELMAN neural network optimized by the sparrow search algorithm (SSA), and reconstructs the results to get initial prediction results. To acquire the final prediction result, local error correction (LEC) is used to perform error correction on the initial prediction model. Five evaluation indicators are used to assess the performance of the suggested coupling model on monthly runoff data from three experimental stations in China. After having analyzed the error correction capability of LEC model, it is found that CEEMDAN-RLMD-SSA-ELMAN-LEC decreases root mean square error (RMSE) values of Manwan Hydropower, Jiayuguan Station, and Yingluoxia Station by 18.52%, 21.78%, and 38.80%, respectively, and increases Nash-Sutcliffe efficiency coefficient (NSEC) values by 2.11%, 4.49%, and 5.43% compared to CEEMDAN-RLMD-SSA-ELMAN without error correction. Therefore, by incorporating error correction, the proposed coupled model is a trustworthy and beneficial means for forecasting monthly runoff.

JAWRA Journal of the American Water Resources Association, 2012

Abstract: With increase in the number and total capacity of hydropower plants in power systems, o... more Abstract: With increase in the number and total capacity of hydropower plants in power systems, optimality algorithms with a single objective are not suitable for optimizing the operation of complex hydropower systems to meet complex demands. Hydropower plants should prioritize discrepant objectives, such as peak regulation and maximizing generation during solving of optimal operation problems of hydropower systems. In this article, we present a multi-step progressive optimality algorithm (MSPOA) for the short-term ...

Molecules, 2023

Recently, much research has revealed the increasing importance of natural fiber in modern applica... more Recently, much research has revealed the increasing importance of natural fiber in modern applications. Natural fibers are used in many vital sectors like medicine, aerospace and agriculture. The cause of increasing the application of natural fiber in different fields is its eco-friendly behavior and excellent mechanical properties. The study’s primary goal is to increase the usage of environmentally friendly materials. The existing materials used in brake pads are detrimental to humans and the environment. Natural fiber composites have recently been studied and effectively employed in brake pads. However, there has yet to be a comparison investigation of natural fiber and Kevlar-based brake pad composites. Sugarcane, a natural fabric, is employed in the present study to substitute trendy materials like Kevlar and asbestos. The brake pads have been developed with 5–20 wt.% SCF and 5–10 wt.% Kevlar fiber (KF) to make the comparative study. SCF compounds at 5 wt.% outperformed the entire NF composite in coefficient of friction (µ), (%) fade and wear. However, the values of mechanical properties were found to be almost identical. Although it has been observed that, with an increase in the proportion of SCF, the performance also increased in terms of recovery. The thermal stability and wear rate are maximum for 20 wt.% SCF and 10 wt.% KF composites. The comparative study indicated that the Kevlar-based brake pad specimens provide superior outcomes compared to the SCF composite for fade (%), wear performance and coefficient of friction (Δμ). Finally, the worn composite surfaces were examined using a scanning electron microscopy technique to investigate probable wear mechanisms and to comprehend the nature of the generated contact patches/plateaus, which is critical for determining the tribological behavior of the composites.

Environmental Monitoring and Assessment, 2023

Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), pla... more Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), planning and operation of solid waste management systems are challenging for decision-makers. In this regard, data-driven methods can be considered robust approaches to modeling this problem. In this paper, three black-box data-driven models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and support vector regression (SVR), and also three whitebox data-driven models, including the M5 model tree (M5MT), classification and regression trees (CART), and group method of data handling (GMDH), were developed for modeling and predicting landfill leachate permeability (k). Based on a previous study conducted by Ghasemi et al. (2021), k can be formulated as a function of impermeable sheets ( IS ) and copper pipes ( CP ). Hence, in the present study, IS and CP were adopted as input variables for the prediction of k and evaluated for the performance of the suggested black-box and white-box data-driven models. Scatter plots and statistical indices such as coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were used for qualitative and quantitative evaluations of the effectiveness of the suggested methods. The outcomes indicated all of the provided models successfully predicted k . However, ANN and GMDH had higher accuracy between the proposed black-box and white-box data-driven models. ANN with R2 = 0.939, RMSE = 0.056, and MAE = 0.017 was marginally better than GMDH with R2 = 0.857, RMSE = 0.064, and MAE = 0.026 in the testing stage. Nevertheless, an explicit mathematical expression provided by GMDH to predict k was easier and more understandable than ANN.

Journal of Hydrology, 2023

Reliable runoff prediction plays a significant role in reservoir scheduling, water resources mana... more Reliable runoff prediction plays a significant role in reservoir scheduling, water resources management, and efficient utilization of water resources. To effectively enhance the prediction accuracy of monthly runoff series, a hybrid prediction model (TVF-EMD-SSA-ELM) combining time varying filtering (TVF) based empirical mode decomposition (EMD), salp swarm algorithm (SSA) and extreme learning machine (ELM) is proposed. Firstly, the monthly runoff series is decomposed into several sub-series using TVF-EMD. Secondly, SSA is used to optimize the input weights and hidden layer biases of the selected ELM model. Finally, the prediction results are generated by summing and reconstructing each sub-series based on the SSA optimized ELM model. This hybrid model is applied to the monthly runoff prediction of Manwan hydropower, Hongjiadu hydropower, and Yingluoxia hydrological station, and compared with back propagation (BP), ELM, SSA-ELM, PSO-ELM, GSA-ELM, TVF-EMD-ELM, EMD-SSA-ELM, extreme-point symmetric mode decomposition (ESMD)-SSA-ELM, wavelet decomposition (WD)-SSA-ELM and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-SSA-ELM models. The prediction performance of various models is reflected by four evaluation indicators (R, NSEC, NRMSE, MAPE). Results reveal that the prediction effect of the ELM model is better than that of BP, the optimization accuracy of SSA is better than those of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and the prediction accuracy of the hybrid TVF-EMD and SSA is better than that of only TVF-EMD or SSA. TVF-EMD-SSA-ELM model has the highest prediction accuracy. When compared with the single ELM model, it's NRMSE and MAPE at Manwan hydropower decrease by 84.4% and 72.38%, those of Hongjiadu hydropower decrease by 85.21% and 78.38%, and those of Yingluoxi hydrological station decrease by 68.42% and 39.51%, respectively. R and NSEC of the three sites are close to 1. Therefore, the proposed model provides a new method for the prediction of monthly runoff, and the results can provide a reference for the prediction of monthly runoff in the study area.

Agronomy , 2023

Applying conventional methods for prediction of environmental impacts in agricultural production ... more Applying conventional methods for prediction of environmental impacts in agricultural production is not actually applicable because they usually ignore other aspects such as useful energy and economic consequence. As such, this article evaluates intelligent models for exergoenvironmental damage and emissions social cost (ESC) for mushroom production in Isfahan province, Iran, by three machine learning (ML) methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR). Accordingly, environmental life cycle damages, cumulative exergy demand, and ESC are examined by the ReCiPe2016 method for 100 tons of mushroom production after data collection by interview. Exergoenvironmental results reveal that, in human health and ecosystems, direct emissions, and resources and exergy categories, diesel fuel and compost are the main hotspots. Economic analysis also shows that total ESC is about 1035$. Results of ML models indicate that ANN with a 6-8-3 structure is the optimum topology for forecasting outputs. Moreover, a two-level structure of ANFIS has weak results for prediction in comparison with ANN. However, support vector regression (SVR) with an absolute average relative error (AARE) (%) between 0.85 and 1.03 (based on specific unit), a coefficient of determination (R2) between 0.989 and 0.993 (based on specific unit), and a root mean square error (RMSE) between 0.003 and 0.011 (based on specific unit) is selected as the best ML model. It is concluded that ML models can furnish comprehensive and applicable exergoenvironmental-economical assessment of agricultural products.

Water, 2023

The reservoir flood control operation problem has the characteristics of multiconstraint, high-di... more The reservoir flood control operation problem has the characteristics of multiconstraint, high-dimension, nonlinearity, and being difficult to solve. In order to better solve this problem, this paper proposes an improved bald eagle search algorithm (CABES) coupled with ε-constraint method (ε-CABES). In order to test the performance of the CABES algorithm, a typical test function is used to simulate and verify CABES. The results are compared with the bald eagle algorithm and particle swarm optimization algorithm to verify its superiority. In order to further test the rationality and effectiveness of the CABES method, two single reservoirs and a multi-reservoir system are selected for flood control operation, and the ε constraint method and the penalty function method (CF-CABES) are compared, respectively. Results show that peak clipping rates of ε-CABES and CF-CABES are both 60.28% for Shafan Reservoir and 52.03% for Dahuofang Reservoir, respectively. When solving the multi-reservoir joint flood control operation system, only ε-CABES flood control operation is successful, and the peak clipping rate is 51.76%. Therefore, in the single-reservoir flood control operation, the penalty function method and the ε constraint method have similar effects. However, in multi-reservoir operation, the ε constraint method is better than the penalty function method. In summary, the ε-CABES algorithm is more reliable and effective, which provides a new method for solving the joint flood control scheduling problem of large reservoirs.