Firdaus Mohamad Hamzah

Long term assessment of air quality from a background station on the Malaysian Peninsula

Rural background stations provide insight into seasonal variations in pollutant concentrations and allow for comparisons to be made with stations closer to anthropogenic emissions. In Malaysia, the designated background station is located in Jerantut, Pahang. A fifteen-year data set focusing on ten major air pollutants and four meteorological variables from this station were analysed. Diurnal, monthly and yearly pollutant concentrations were derived from hourly continuous monitoring data. Statistical methods employed included principal component regression (PCR) and sensitivity analysis. Although only one of the yearly concentrations of the pollutants studied exceeded national and World Health Organisation (WHO) guideline standards, namely PM10, seven of the pollutants (NO, NO2, NOx, O3, PM10, THC and CH4) showed a positive upward trend over the 15-year period. High concentrations of PM10 were recorded during severe haze episodes in this region. Whilst, monthly concentrations of most air pollutants, such as: PM10, O3, NOx, NO2, CO and NmHC were recorded at higher concentrations between June and September, during the southwest monsoon. Such results correspond with the mid-range transport of pollutants from more urbanised and industrial areas. Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate the influence of local traffic emissions on air quality at the Jerantut background station. Although the pollutant concentrations have not shown a rapid increase, an alternative background station will need to be assigned within the next decade if development projects in the surrounding area are not halted.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region

Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model’s efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month−1), root mean square error (1.14 BCM month−1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region.

Assessing the responses of physical parameters in ocean via statistical approach

It is essential to assess a physical parameter in response to the changes in other physical parameter. Exploration on the type of relationship between two physical parameters depends on models and expert view due to the complexity of the ecosystems. These need validation with actual data over a certain periods. Innovative statistical approaches, particularly the nonparametric regression is presented to investigate the ecological relationships. These are achieved by demonstrating the features of salinity, conductivity and temperature at a sampling point in Selat Tebrau. Observed data monitored for 10 years from 2004–2013 are examined. Testing for no-effect and linearity for salinity and temperature; log conductivity and temperature, and log conductivity and salinity, with the ecological objectives of investigating the evidence of changes in each of the above physical parameter. The results show the appropriateness of smooth function to explain the variation of salinity in response to the changes in tempera...

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.

Operating a reservoir system based on the shark machine learning algorithm

The operating process of a multi-purpose reservoir needs to develop models that have the ability to overcome the challenges facing the decision makers. Therefore, the development of a mathematical optimization model is crucial for selecting the optimal policies for the reservoir operation. In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. The SMLA began with a group of randomly produced potential solutions and later interactively executed the search for the optimal solution. The procedure for the SMLA is suitable to be applied to a reservoir system due to its ability to tackle the stochastic features of dam and reservoir systems. The major purpose of the proposed models is to generate an operation rule that could minimize the absolute value of the differences between water release and water demand. The proposed model has been examined using the data of the Aswan High Dam, Egypt as the case study. The performance of the SMLA was compared with the performance of the most widespread evolutionary algorithms, namely, the genetic algorithm (GA). Comprehensive analysis of the results was performed using three performance indicators, namely, resilience, reliability, and vulnerability. This work concluded that the performance of the SMLA model was better than the GA model in generating the optimal policy for reservoir operation. The result showed that the SMLA succeeded in providing high reliability (99.72%), significant resilience (1) and minimum vulnerability (20.7% of demand).

Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance

Abstract Obtaining successful operation rules for dam and reservoir systems is crucial for improving water management to meet the increase in agricultural, domestic and industrial activities. Several research efforts have been developed to generate optimal operation rules for dam and reservoir systems utilizing different optimization algorithms. The main purpose of an operation rule is to minimize the gap between water supply and water demand patterns. To examine the optimized model performance, the simulation of a dam and reservoir system is usually carried out for a particular period utilizing the generated operation rule. During the simulation procedure, although reservoir inflow and evaporation are stochastic variables that are required to be forecasted during simulation, they are considered deterministic variables. This study attempts to integrate a forecasting model for reservoir inflow and evaporation with the operation rules generated from optimization models during the simulation procedure. The present study employs several optimization models to generate an optimal operation rule and two different forecasting models for reservoir inflow and reservoir evaporation. The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). Two different forecasting models have been developed for reservoir inflow and evaporation using the radial basis function neural network (RBF-NN) and support vector regression (SVR). It is necessary to analyze the proposed simulation procedure for examining the operation rule to comprehend the analysis under different optimal operation rules and levels of accuracy for both hydrological variables. The suggested models have been applied to generate optimal operation policies and reservoir inflow and evaporation forecasts for the Timah Tasoh dam (TTD) located in Malaysia. The results show that the major findings regarding the model performance during the simulation period indicate the necessity to pay attention to evaluating the optimized model performance by considering the results of the forecasting model for both the hydrological variables of reservoir inflow and reservoir evaporation rather than the deterministic values.

Temporal Analysis of Water Quality in The Straits of Johor

Temporal analysis is used to obtain changes in water quality along the Straits of Johor. Boxplot is used to show graphically on the temporal pattern of yearly and monthly temperature, conductivity, pH, dissolved oxygen (DO), total suspended solids (TSS) and oil and grease in the water from 2003 to 2013. The ANOVA test is carried out to determine the significance of the mean of each parameter between years. The boxplot shows a particular trend for each of the marine parameters over the year and the seasonal pattern is only apparent in temperature. The ANOVA test indicates a significant difference in the means of each water quality marine parameter across the year. The changes in each of the marine parameters over the year could be associated to the natural features of the marine water and climate change. The seasonal pattern however is only apparent in temperature which could be influenced by the monsoon seasons.

Aggregate Impact on Dynamic Behavior of Concrete using Finite Element Method

Konkrit merupakan bahan komposit yang penting dan sering digunakan dalam sektor pembinaan kerana konkrit mempunyai sifat kekuatan mampatan yang tinggi. Agregat terdiri daripada 70% isi padu konkrit, maka sifat agregat dipercayai memberi kesan yang besar terhadap tingkap laku konkrit semasa struktur konkrit dikenakan beban dinamik yang kuat (contohnya letupan gas atau senjata, kejatuhan batu dan gempa bumi). Kelakuan konkrit di bawah beban dinamik boleh diketahui dengan mengenal pasti faktor peningkatan dinamik (DIF). Semasa konkrit di bawah tekanan statik yang rendah, corak keretakan konkrit berlaku pada zon peralihan antara muka (ITZ) tetapi jika konkrit di bawah tekanan yang tinggi, keretakan berlaku melalui agregat, maka ia akan meningkatkan kekuatan konkrit. Penyelidikan ini bermatlamat untuk mengkaji kesan agregat biasa ke atas kelakuan dinamik konkrit dengan menggunakan kaedah unsur terhingga yang menggunakan perisian LS-DYNA. Model konkrit yang berlainan agregat iaitu agregat granit, basalt, batu kapur, dan batu pasir telah digunakan dalam kajian ini. Simulasi berangka model konkrit di bawah mampatan pada kadar tekanan yang berbeza dijalankan. Pada kadar terikan yang rendah, kerosakan pada konkrit berlaku pada mortar sahaja dan tidak ketara. Manakala, pada terikan yang tinggi, kerosakan berlaku pada agregat dan juga mortar dan kerosakan adalah menonjol. Basalt mempunyai kekuatan yang tinggi mempunyai nilai DIF yang paling tinggi berbanding dengan agregat yang lain di mana batu kapur mencatatkan nilai DIF yang paling rendah. Kata kunci: Agregat; faktor peningkatan dinamik; kadar terikan

On linear and oblong leaves shape model

The aim of this paper is to develop and represent the overall linear and oblong leaves shape model for a given botanical textual description. For this purpose, the B-spline generative shape method (GSM) was used where the tip, side and the base of the linear and oblong leaves were assembled. Using the GSM and the leaf shape synthesis system, the tip and the base will be automatically generated preserving C2 continuity to ensure a reasonable shape in proportion. Expert botanists were referred to assess the drawing results. The findings show that the use of GSM produced good result except for some cases where the drawing produced minor visual error. As a conclusion, the GSM is a very practical way to model the linear and the oblong leaf shape. As the implication, both the GSM and the system allow the botanists to readily see a picture which might have been hard to visualize before.