A cylindrical data set consists of circular and linear variables. We focus on developing an outlier detection procedure
for cylindrical regression model proposed by Johnson and Wehrly (1978) based on the k-nearest neighbour approach.
The procedure is applied based on the residuals where the distance between two residuals is measured by the Euclidean
distance. This procedure can be used to detect single or multiple outliers. Cut-off points of the test statistic are generated
and its performance is then evaluated via simulation. For illustration, we apply the test on the wind data set obtained
from the Malaysian Meteorological Department.
The complexity of the coastal environment and the advent of climate change cause coastal erosion, which is incontrovertibly a significant concern worldwide, including Peninsular Malaysia, where, the coast is threatened by severe erosion linked to anthropogenic factors and monsoonal wind-driven waves. Consequently, the Malaysian government implemented a mitigation plan using several coastal defence systems to overcome the coastal erosion problem. This study assesses coastal erosion management strategies along a monsoon-dominated coasts by evaluating the efficacy of coastal protection structures against the coast. To this end, we analysed 244 km of the coastline of Terengganu, a federal state located on the east coast of Peninsular Malaysia. Due to a higher frequency of storms and the ensuing inception of high wave energy environments during the northeast monsoon (relative to southwest monsoon), the study area is the most impacted region in Malaysia with regard to coastal erosion. Fifty-five (55) coastal defence structures were detected along the Terengganu coastline. The Digital Shoreline Analysis System (DSAS) was utilised to compute changes in the rate statistics for various historical shoreline positions along the Terengganu coast to assess the efficacy of the defence structures. Additionally, this study acquired the perception of the existing coastal management strategies through an interview session with the concerned stakeholders. The rate statistics revealed the effectiveness and impact of the coastal defence structure on the coastline. Assessing the functionality of the coastal defence structures shed light on the present scenario of coastal erosion management. Greater efficacy and lower impact of coastal defence structures are prescribed for coastal erosion management strategies across the monsoon-dominated coast.
Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
It has been shown in previous studies that the flight trajectories of sports balls are influenced by their aerodynamic characteristics. These aerodynamic characteristics are primarily dependent on the physical shape and surface texture of the balls. Even though sepak takraw is well established as a sport, little is known regarding the aerodynamic characteristics of the sepak takraw ball, which has a rather complex shape and surface texture. Hence, the main objective of this research is to investigate the aerodynamic characteristics (specifically the drag and lift coefficients) and flow features of a modern sepak takraw ball commercially available in the market by means of numerical simulations and wind tunnel experiments using the smoke flow visualization technique. The aerodynamic characteristics and flow features of the ball are determined for non-spinning conditions at a wind speed of 3 m/s. It is found that the drag coefficient and lift coefficient of the sepak takraw ball is 0.4868400 and - 0.0130915, respectively. The images captured from the smoke flow visualization experiments reveal that the sepak takraw ball is in the subcritical flow regime at a wind speed of 3 m/s, which is the regime before the drag crisis. The laminar boundary layer separates from the upper and lower surfaces of the ball at points upstream of the equator of the ball, creating a large wake region downstream of the sepak takraw ball and resulting in high drag. This in turn, influences the trajectory of the sepak takraw ball in flight. The flow features observed from the smoke flow visualization experiments are representative of the flow during a sepak takraw game. Owing to the complexity of sepak takraw ball, it is recommended that the aerodynamic characteristics of the sepak takraw ball are investigated for spinning conditions in future studies.
The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.
In summer 2010, a massive bloom appeared in the middle (16-25°N, 160-200°E) of the North Pacific Subtropical Gyre (NPSG) creating a spectacular oasis in the middle of the largest oceanic desert on Earth. Peaked in June 2010 covering over two million km2 in space, this phytoplankton bloom is the largest ever recorded by ocean color satellites in the NPSG over the period from 1997 to 2013. The initiation and mechanisms sustaining the massive bloom were due to atmospheric and oceanic anomalies. Over the north (25-30°N) of the bloom, strong anticyclonic winds warmed sea surface temperature (SST) via Ekman convergence. Subsequently, anomalous westward ocean currents were generated by SST meridional gradients between 19°N and 25°N, producing strong velocity shear that caused large number of mesoscale (100-km in order) cyclonic eddies in the bloom region. The ratio of cyclonic to anticyclonic eddies of 2.7 in summer 2010 is the highest over the 16-year study period. As a result of the large eddy-number differences, eddy-eddy interactions were strong and induced submesoscale (smaller than 100 km) vertical pumping as observed in the in-situ ocean profiles. The signature of vertical pumping was also presented in the in-situ measurements of chlorophyll and nutrients, which show higher concentrations in 2010 than other years.
In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.
Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.
Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.
A comprehensive study of the chemical composition of rainwater was carried out from October 2016 to September 2017 in the equatorial tropical rainforest region of northwestern Borneo. Monthly cumulative rainwater samples were collected from different locations in the Limbang River Basin (LRB) and were later categorized into seasonal samples representing northeast monsoon (NEM), southwest monsoon (SWM), and inter-monsoon (IM) periods. Physical parameters (pH, EC, TDS, DO, and turbidity), major ions (HCO3-, Cl-, Ca2+, Mg2+, Na+, and K+) and trace metals (Co, Ni, Cd, Fe, Mn, Pb, Zn, and Cu) were analyzed from collected rainwater samples. Rainwater is slightly alkaline with mean pH higher than 5.8. Chloride and bicarbonate are the most abundant ions, and the concentration of major ions in seasonal rainwater has shown slight variation which follows a descending order of HCO3-> Cl-> Na+ > Ca2+ > Mg2+ > K+ in NEM and Cl- > HCO3- > Na+ > Ca2+ > K+ > Mg2+ in SWM and Cl- > HCO3- > Na+ > Ca2+ > Mg2+ > K+ in IM period. Trace metals such as Fe and Ni have shown dominance in seasonal rainwater samples, and all the metals have shown variation in concentration in different seasons. Variation in chemical characteristic of seasonal rainwater samples identified through piper diagram indicates dominance of Ca2+-Mg2+-HCO3- and mixed Ca2+-Mg2+-Cl- facies during NEM, SWM, and IM periods. Statistical analysis of the results through two-way ANOVA and Pearson's correlation also indicates significant variation in physico-chemical characteristics. This suggests a variation in contributing sources during the monsoon seasons. Factor analysis confirmed the source variation by explaining the total variance of 79.80%, 90.72%, and 90.52% with three factor components in NEM, SWM, and IM rainwater samples with different loading of parameters. Enrichment factor analysis revealed a combined contribution of marine and crustal sources except K+ which was solely from crustal sources. Sample analysis of backward air mass trajectory supports all these findings by explaining seasonal variation in the source of pollutants reaching the study area. Overall, the results show that the chemical composition of seasonal rainwater samples in LRB was significantly influenced by natural as well as anthropogenic processes. These include (long-range and local) industrial activities, fossil fuel combustion, forest burning, transportation activities including road transport and shipping activities, and land-derived soil dust along with chemical constituents carried by seasonal wind.
Meteorology over coastal region is a driving factor to the concentration of air particles and reactive gases. This study aims to conduct a research to determine the level of year-round air particles and the interaction of the meteorological driving factors with the particle number and mass in 2018, which is moderately influenced by Southeast Asian haze. We obtained the measurement data for particle number count (PNC), mass, reactive gases, and meteorological factors from a Global Atmospheric Watch (GAW) station located at Bachok Marine Research Center, Bachok, Kelantan, Malaysia. For various timeseries and correlation analyses, a 60-second resolution of the data has been averaged hourly and daily and visualized further. Our results showed the slight difference in particle behavior that is either measured by unit mass or number count at the study area. Diurnal variations showed that particles were generally high during morning and night periods. Spike was observed in August for PM2.5/PNC2.5 and PM10/PNC10 and in November for PMCoarse/PNCCoarse. From a polar plot, the particles came from two distinct sources (e.g., seaside and roadside) at the local scale. Regional wind vector shows two distinct wind-blown directions from northeast and southwest. The air mases were transported from northeast (e.g., Philippines, mainland China, and Taiwan) or southwest (e.g., Sumatra) region. Correlation analysis shows that relative humidity, wind direction, and pressure influence the increase in particles, whereas negative correlation with temperature is observed, and wind speed may have a potential role on the decline of particle concentration. The particles at the study area was highly influenced by the changes in regional wind direction and speed.
Coral spawning times have been linked to multiple environmental factors; however, to what extent these factors act as generalized cues across multiple species and large spatial scales is unknown. We used a unique dataset of coral spawning from 34 reefs in the Indian and Pacific Oceans to test if month of spawning and peak spawning month in assemblages of Acropora spp. can be predicted by sea surface temperature (SST), photosynthetically available radiation, wind speed, current speed, rainfall or sunset time. Contrary to the classic view that high mean SST initiates coral spawning, we found rapid increases in SST to be the best predictor in both cases (month of spawning: R(2) = 0.73, peak: R(2) = 0.62). Our findings suggest that a rapid increase in SST provides the dominant proximate cue for coral mass spawning over large geographical scales. We hypothesize that coral spawning is ultimately timed to ensure optimal fertilization success.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
The Malacca Straits is located between Peninsula Malaysia and Sumatra Island. This investigation used equation of motion (Navier-Stokes equation) with the following driving forces: tides, wind of National Centers for Environmental Prediction (NCEP) for year of 2007, salinity and temperature. The equation of motion was solved by means of Hamburg Shelf Ocean Model (HAMSOM). The results for both southwest and northeast monsoon were explained and discussed. The simulation results both for February and August 2007 were relatively similar. Current surface simulation in the Malacca Straits agrees well with the current pattern of previous works. The magnitude of current was between 10-70 cm/s to the northwest. While at the layer 30-50 m in the Malacca Straits, the currents have the magnitude of 10-30 cm/s towards northwest. For the bottom current, the current speed was 0-20 cm/s towards northwest. For the surface and 30-50 m layer, generally the current magnitudes were greater in February compared to those in August. While for the bottom layer, the current magnitudes between February and August were relatively the same.
Renewable energy assessments for resort islands in the South China Sea were conducted that involves the collection and analysis of meteorological and topographic data. The meteorological data was used to assess the PV, wind and hydropower system potentials on the islands. Furthermore, the reconnaissance study for hydro-potentials were conducted through topographic maps in order to determine the potential sites suitable for development of run-of-river hydropower generation. The stream data was collected for 14 islands in the South China Sea with a total of 51 investigated sites. The data from this study are related to the research article "Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea" published in Energy (Khan et al., 2015) [1].
The aim of the measurement of this data is to evaluate the Indoor Air Quality (IAQ) in terms of chemical and physical parameters. Data were collected at three different kindergartens having different surrounding activities (industrial, institutional, residential area). The chemical parameters measured were respirable suspended particulates of PM10, PM2.5, PM1, carbon monoxide and carbon dioxide, and the concentrations are within the acceptable limit. Physical parameters of wind speed are within the standard, while temperature and relative humidity exceeded the acceptable limit. A strong correlation was found between the chemical IAQ parameters with thermal comfort parameters (temperature and relative humidity). The concentration of IAQ pollutants is higher in order of residential > institutional > industrial.
Natural ventilation is defined as the number of air exchanges per hour per unit floor area necessary
to reduce high indoor air temperature and humidity. In addition, it maintains the concentration of carbon dioxide. Natural ventilation is preferred in mechanical system as the ventilation opening is built into the greenhouse, with lower construction cost and no energy and maintenance inputs are required. A mathematical model to quantify natural ventilation rates was developed and verified in large-scale greenhouse structures. For this purpose, four Naturally Ventilated Tropical Greenhouse Structures were designed and constructed at the Malaysian Agricultural Research and Development Institute (MARDI). These were single, double, triple, and quadruple span structures with floor areas of 500 m2, 1000 m2, 1500 m2 and 2000 m2, respectively. This paper presents the validation of a mathematical model which was developed to quantify natural ventilation rates which are very crucial to reduce high in-house temperature built up in the tropics. Regression equations of natural ventilation against wind speed were found to be Φw = 0.0632V, Φw= 0.0395V, Φw= 0.0316Vand Φw=0.0276V for the single, double, triple and quadruple spans, respectively. Meanwhile, coefficients of determination showed strong relationships between ventilation rate and wind speed, with R2 = 0.9999 for all structures. Larger floor area was found to have higher in-house temperature than smaller ones. Ventilation rate inside the single-span structure was found to be higher compared to the multi-span structures, which increased linearly with the increasing wind speed at the eaves of structure.