Research Papers - Department of Civil Engineering

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Author: search.filters.author.Azamathulla, H

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    PublicationOpen Access
    A Simplified Mathematical Formulation for Water Quality Index (WQI): A Case Study in the Kelani River Basin, Sri Lanka
    (Fluids, 2022-04-23) Meddage, D. P. P; Azamathulla, H; Pandey, M; Rathnayake, U; Makumbura, R. K
    Surface water quality is degraded due to industrialization; however, it is one of the widely used sources for water supply systems worldwide. Thus, the polluted water creates significant issues for the health of the end users. However, poor attention and concern can be identified on this important issue in most developing countries, including Sri Lanka. The Kelani River in Sri Lanka is the heart of the water supply of the whole Colombo area and has the water intake for drinking purposes near an industrialized zone (Biyagama). Therefore, this study intends to analyze the effect of industrialization on surface water quality variation of the Kelani River basin in Sri Lanka in terms of the water quality index (WQI). We proposed a regression model to predict the WQI using the water quality parameters. Nine water quality parameters, including pH, total phosphate, electric conductivity, biochemical oxygen demand, temperature, nitrates, dissolved oxygen, chemical oxygen demand, and chlorine evaluated the Kelani River water quality. The proposed regression model was used to examine the water quality of samples obtained at twelve locations from January 2005 to December 2012. The highest WQI values were found in Raggahawatte Ela throughout the 8 years, located near the Biyagama industrial zone. The relationship of industries to water quality in the Kelani River is stated. The surface water quality gradually decreased as a result of development and industrialized activities. Therefore, this work showcases and recommends the importance of introducing necessary actions and considerations for future water management systems.
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    PublicationOpen Access
    Interpretation of Machine-Learning-Based (Black-box) Wind Pressure Predictions for Low-Rise Gable-Roofed Buildings Using Shapley Additive Explanations (SHAP)
    (2022-05-29) Meddage, P; Ekanayake, I; Perera, U. S; Azamathulla, H; Md Said, M. A; Rathnayake, U
    Conventional methods of estimating pressure coefficients of buildings retain time and cost constraints. Recently, machine learning (ML) has been successfully established to predict wind pressure coefficients. However, regardless of the accuracy, ML models are incompetent in providing end-users’ confidence as a result of the black-box nature of predictions. In this study, we employed tree-based regression models (Decision Tree, XGBoost, Extra-tree, LightGBM) to predict surface-averaged mean pressure coefficient (Cp,mean), fluctuation pressure coefficient (Cp,rms), and peak pressure coefficient (Cp,peak) of low-rise gable-roofed buildings. The accuracy of models was verified using Tokyo Polytechnic University (TPU) wind tunnel data. Subsequently, we used Shapley Additive Explanations (SHAP) to explain the black-box nature of the ML predictions. The comparison revealed that tree-based models are efficient and accurate in wind-predicting pressure coefficients. Interestingly, SHAP provided human-comprehensible explanations for the interaction of variables, the importance of features towards the outcome, and the underlying reasoning behind the predictions. Moreover, SHAP confirmed that tree-based predictions adhere to the flow physics of wind engineering, advancing the fidelity of ML-based predictions.
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    PublicationOpen Access
    Comparison of different Artificial Neural Network (ANN) training algorithms to predict atmospheric temperature in Tabuk, Saudi Arabia
    (researchgate.net, 2020-06) Perera, A; Azamathulla, H; Rathnayake, U
    Use of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare different ANN models to predict the minimum atmospheric temperature and maximum atmospheric temperature in Tabuk, Saudi Arabia. ANN models were trained using eight different training algorithms. BFGS Quasi Newton (BFG), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquadt (LM), Scaled Conjugate Gradient (SCG), Fletcher-Reeves update Conjugate Gradient algorithm (CGF), One Step Secant (OSS), Polak-Ribiere update Conjugate Gradient (CGP) and Resilient Back-Propagation (RP) training algorithms were fed to the climatic data in Tabuk, Saudi Arabia. The performance of the different training algorithms to train ANN models were evaluated using Mean Squared Error (MSE) and correlation coefficient (R). The evaluation shows that training algorithms BFG, LM and SCG have outperformed others while OSS training algorithm has the lowest performance in comparison to other algorithms used.
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    PublicationOpen Access
    Recent Climatic Trends In Trinidad And Tobago, West Indies
    (Research and Technology Transfer Affairs Division,, 2020-02) Perera, A; Mudannyake, S; Azamathulla, H; Rathnayake, U. S
    Seawater level rise is one of the most prevalent adverse environmental impacts of the ongoing global warming process. Island nations are more vulnerable to the impact than the land masses. Two such islands impacted by global warming are Trinidad and Tobago, located in the Atlantic Ocean. However, there is minimal related research in this area in the context of the impact of climate variability. Therefore, it is timely and interesting to assess the climatic trends in islands that are extremely vulnerable like Trinidad and Tobago. This paper presents a detailed non-parametric statistical analysis for well-known climate gauges in Trinidad and Tobago, West Indies. Mann Kendall and Sen’s slope tests were carried out on two identified rain gauges in Trinidad and Tobago. Monthly climatic data including cumulative rainfall and the average of the minimum and maximum atmospheric temperatures were processed to identify the trend analysis using the above stated non-parametric tests. Important results are found from the analysis; most importantly, there is no significant impact on the rainfall in the area due to the climate variability over 30 years. However, the atmospheric temperature behaves in a different way and it has a rising pattern across the total 12 months studied. This can be seen for both the minimum and maximum atmospheric temperatures. Therefore, the warm months are becoming warmer and the cold months are becoming less cold. This is a critical finding that must be considered for any future planning processes.