Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia

The declining of air quality mostly affects the elderly, children, people with asthma, as well as a restriction on outdoor activities. Therefore, there is an importance to provide a statistical modelling to forecast the future values of surface layer ozone (O3) concentration. The objectives of this study are to obtain the best multivariate time series (MTS) model and develop an online air quality forecasting system for O3 concentration in Malaysia. The implementations of MTS model improve the recent statistical model on air quality for short-term prediction. Ten air quality monitoring stations situated at four (4) different types of location were selected in this study. The first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban located in Banting, Kangar, and Tanjung Malim, also the only background station at Jerantut. The hourly record data from 2010 to 2017 were used to assess the characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH) were used to examine the best MTS models. Three methods of MTS namely vector autoregressive (VAR), vector moving average (VMA), and vector autoregressive moving average (VARMA), has been applied in this study. Based on the performance error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained the VMA(2) as the best model. The lowest root mean square error (RMSE) and normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system for O3 was successfully developed based on the best MTS models to represent each monitoring station

Mechanical properties of the concrete containing porcelain waste as sand

The demand of concrete have been increases on a daily bases which consume a lot of natural resource such as sand and gravel, there is an immediate need for finding suitable alternative which can be used to replace sand partially with another materials with high propor-tion . Ceramic waste is one of the strongest research areas that include the activity of replacement in all the sides of construction materi-als. This research aims to improve the performance of concrete using ceramic waste, and demonstrate the performance of mechanical properties to the concrete with partial replacement of sand by using waste porcelain. For these, we analyzed the mechanical properties of the concrete such as compressive strength, split tensile and flexural strength, the specimen were measured based on 10% ,20% ,30% ,40%, and 50% weight ratio of replace sand with waste porcelain at different time under water for 7 days , 28 days , 60 days . The optimum consideration were given to mechanical properties of the concrete, at different amount of ceramic waste as sand

Diversifying search in bee algorithms for numerical optimisation

© Springer Nature Switzerland AG 2018. Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks

A honeybees-inspired heuristic algorithm for numerical optimisation

© 2019, The Author(s). Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature