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result(s) for
"Jayawardena, A. W"
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Climate change — Is it the cause or the effect?
Climate change and global warming are currently hot topics. There is no doubt that warming is taking place in some parts of the globe as evidenced by melting of ice caps and glaciers, sea level rises, temperature rises, among other changes. At the same time, skeptics are of the view that the issue is blown out of proportion, and that warming exists locally and that it is premature to conclude that it is a global phenomenon. Projections made into the future climate have many uncertainties. These include model uncertainties, data length and their representativeness, calibration and validation issues, and the logic of projecting into 100 years or more into the future with a relatively short window of observations. What is not made known to the public at large is a balanced view that highlights the real scientific evidence and limitations and uncertainties of their findings. As a result the general public do not get a balanced view of the issues and get to know only a partial truth and not the whole truth. In this paper, an attempt is made to revisit the issues in the light of available information, and to highlight the important role of increasing population, with the objective of giving a reasonably balanced view since opinions about the issues are either biased or divided.
Journal Article
Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model
In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting.
Journal Article
Evolutionary product unit based neural networks for hydrological time series analysis
Artificial Neural Networks (ANNs) are now widely used in many areas of science, medicine, finance and engineering. Analysis and prediction of time series of hydrological/and meteorological data is one such application. Problems that still exist in the application of ANN's are the lack of transparency and the expertise needed for training. An evolutionary algorithm-based method to train a type of neural networks called Product Units Based Neural Networks (PUNN) has been proposed in a 2006 study. This study investigates the applicability of this type of neural networks to hydrological time series prediction. The technique, with a few small changes to improve the performance, is applied to some benchmark time series as well as to a real hydrological time series for prediction. The results show that evolutionary PUNN produce more transparent models compared to widely used multilayer perceptron (MLP) neural network models. It is also seen that training of PUNN models requires less expertise compared to MLPs.
Journal Article
Nutrient load estimation in nonpoint source pollution of Hong Kong region
Red tides and eutrophication have been frequently observed over the past two decades in coastal waters around Hong Kong, which are caused by many factors and one of them is the nutrient from nonpoint source pollution (NSP). This paper concentrates on the nutrients carried by river flow from watersheds. Since there are no systematical data sets of nonpoint source pollution in Hong Kong, monthly river water quality measurements, rainfall and river flow data, land uses, and other related information are used to analyze the characteristics of NSP and estimate the nutrient loads for Hong Kong region. Main achievements are as follows: firstly, besides mean concentration for single land use, the concept of integrated mean concentration for mixed land uses was proposed and applied. Secondly, mean concentrations were carried out for different land uses (agriculture, town, grassland, shrubland and woodland), each Water Control Zone, and Hong Kong region. Thirdly, the annual nutrient loads were estimated, for the first time in this paper, with various methods for the whole area of Hong Kong, and about 8,000 tons of TN and 1,500 tons TP are transported into coastal waters from Hong Kong's land in 1998.
Journal Article
Rainfall data simulation by hidden Markov model and discrete wavelet transformation
In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data.
Journal Article
Use of a supercomputer to advance parameter optimisation using genetic algorithms
Parameter optimisation is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents two modifications to achieve optimised results for a Tank Model in less computational time. Firstly, a modified genetic algorithm (GA) is developed to enhance the fitness of the population consisting of possible solutions in each generation. Then the parallel processing capabilities of an IBM 9076 SP2 computer are used to expedite implementation of the GA. A comparison of processing time between a serial IBM RS/6000 390 computer and an IBM 9076 SP2 supercomputer reveals that the latter can be up to 8 times faster. The effectiveness of the modified GA is tested with two Tank Models for a hypothetical catchment and a real catchment. The former showed that the parallel GA reaches a lower overall error in reduced time. The overall RMSE, expressed as a percentage of actual mean flow rate, improves from 31.8% in a serial processing computer to 29.5% on the SP2 supercomputer. The case of the real catchment – Shek-Pi-Tau Catchment in Hong Kong – reveals that the supercomputer enhances the swiftness of the GA and achieves its objective within a couple of hours.
Journal Article
Early Detection of Furniture-Infesting Wood-Boring Beetles Using CNN-LSTM Networks and MFCC-Based Acoustic Features
Structural pests, such as termites, pose a serious threat to wooden buildings, resulting in significant economic losses due to their hidden and progressive damage. Traditional detection methods, such as visual inspections and chemical treatments, are invasive, labor intensive, and ineffective for early stage infestations. To bridge this gap, this study proposes a non invasive deep learning based acoustic classification framework for early termite detection. We aim to develop a robust, scalable model that distinguishes termite generated acoustic signals from background noise. We introduce a hybrid Convolutional Neural Network Long Short Term Memory architecture that captures both spatial and temporal features of termite activity. Audio data were collected from termite infested and clean wooden samples. We extracted Mel Frequency Cepstral Coefficients and trained the CNN LSTM model to classify the signals. Experimental results show high performance, with 94.5% accuracy, 93.2% precision, and 95.8% recall. Comparative analysis reveals that the hybrid model outperforms standalone CNN and LSTM architectures, underscoring its combined strength. Notably, the model yields low false-negative rates, which is essential for enabling timely intervention. This research contributes a non invasive, automated solution for early termite detection, with practical implications for improved pest monitoring, minimized structural damage, and better decision making by homeowners and pest control professionals. Future work may integrate IoT for real time alerts and extend detection to other structural pests.
Paper
Dynamics of Hydro-Meteorological and Environmental Hazards
An overview is presented of the physical and biological factors that cause disasters and of their relationships in quantitative terms to the outcomes of these disasters. The chapter begins with an introduction to the atmosphere, which is the starting point of all hydro-meteorological disasters, including the different processes and links that lead to precipitation. The relationship between precipitation and runoff, or floods, including their forecasting techniques is described. The chapter also covers the types and causes of water-related environmental disasters. A quantitative description of mixing processes by Fickian diffusion and by convective dispersion is given. The governing equations and simplifications for conservative and non-conservative types of pollutants, point and non-point sources of pollution, reaction kinetics for non-conservative pollutants, and modeling approaches, are presented. As the health of a waterbody is measured by the dissolved oxygen concentration, an introduction to the oxygen sag curve in rivers is also given.
Book Chapter