Displaying publications 41 - 60 of 258 in total

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  1. Fulltext Conceptual model for a sustainable Crowdsourcing ecosystem
    Hassan, H., Aris, S.R.S., Arshad, N.H., Janom, N., Salleh, S.S.
    Pertanika Journal of Science & Technology, 2017;25(110):89-98.
    MyJurnal
    Crowdsourcing has changed the way people conduct business. It provides access to work, and employers can source for the best talent, at the best price, with the shortest turnaround time. Research so far has focussed on crowdsourcing implementation. Hence, there is a need to conduct research that can contribute towards crowdsourcing sustainability. Thus, the objectives of this paper are to identify current practices of crowdsourcing in Malaysia and the challenges that face it. A conceptual model for crowdsourcing sustainability ecosystem is then proposed. This study adopted the case-study approach. Two crowdsourcing platforms were examined in the case study. Two techniques were used to obtain the data: observation and interview. Observation was carried out to observe how the crowdsourcing platforms worked. The interviews helped to uncover current practices, challenges in using crowdsourcing and identification of sustainability factors. It is hoped that the proposed conceptual model will facilitate better planning of the ecosystem supporting crowdsourcing and ensure sustainable growth for crowdsourcing. Future research into crowdsourcing can test the proposed conceptual model to validate its components.
    Matched MeSH terms: Forecasting
  2. Fulltext Consolidated backpropagation neural network for Malaysian construction costs indices data with outliers problem
    Saadi Ahmad Kamaruddin, Nor Azura Md Ghani, Norazan Mohamed Ramli
    Pertanika Journal of Science & Technology, 2018;26(1):353-366.
    MyJurnal
    Neurocomputing has been adjusted effectively in time series forecasting activities, yet the vicinity of exceptions that frequently happens in time arrangement information might contaminate the system preparing information. This is because of its capacity to naturally realise any example without earlier suspicions and loss of sweeping statement. In principle, the most widely recognised calculation for preparing the system is the backpropagation (BP) calculation, which inclines toward minimisation of standard slightest squares (OLS) estimator, particularly the mean squared mistake (MSE). Regardless, this calculation is not by any stretch of the imagination strong when the exceptions are available, and it might prompt bogus expectation of future qualities. In this paper, we exhibit another calculation which controls the firefly algorithm of least median squares (FFA-LMedS) estimator for neural system nonlinear autoregressive moving average (ANN-NARMA) model enhancement to provide betterment for the peripheral issue in time arrangement information. Moreover, execution of the solidified model in correlation with another hearty ANN-NARMA models, utilising M-estimators, Iterative LMedS and Particle Swarm Optimisation on LMedS (PSO-LMedS) with root mean squared blunder (RMSE) qualities, is highlighted in this paper. In the interim, the actual monthly information of Malaysian Aggregate, Sand and Roof Materials value was taken from January 1980 to December 2012 (base year 1980=100) with various levels of anomaly issues. It was found that the robustified ANN-NARMA model utilising FFA-LMedS delivered the best results, with the RMSE values having almost no mistakes at all in all the preparation, testing and acceptance sets for every single distinctive variable. Findings of the studies are hoped to assist the regarded powers including the PFI development tasks to overcome cost overwhelms.
    Matched MeSH terms: Forecasting
  3. Fulltext Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for forecasting oil production
    Suhartono, Prastyo, Dedy Dwi, Kuswanto, Heri, Muhammad Hisyam Lee
    MATEMATIKA, 2018;34(1):103-111.
    MyJurnal
    Monthly data about oil production at several drilling wells is an example of
    spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal
    model, i.e. Feedforward Neural Network - VectorAutoregressive (FFNN-VAR) and FFNN
    - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast
    accuracy to linearspatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal
    models are proposed and applied for forecasting monthly oil production data at three
    drilling wells in East Java, Indonesia. There are 60 observations that be divided to two
    parts, i.e. the first 50 observations for training data and the last 10 observations for
    testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear
    spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11)
    as linear spatio-temporal models. Moreover, further research about nonlinear spatiotemporal
    models based on neural networks and GSTAR is needed for developing new
    hybrid models that could improve the forecast accuracy.
    Matched MeSH terms: Forecasting
  4. Fulltext Performance evaluation of a new hybrid multivariate meteorological model analysis: a simulation study
    Siti Mariam Norrulashikin, Fadhilah Yusof, Kane, Ibrahim Lawal
    MATEMATIKA, 2018;34(1):73-85.
    MyJurnal
    Simulation is used to measure the robustness and the efficiency of the forecasting
    techniques performance over complex systems. A method for simulating multivariate
    time series was presented in this study using vector autoregressive base-process. By
    applying the methodology to the multivariable meteorological time series, a simulation
    study was carried out to check for the model performance. MAPE and MAE performance
    measurements were used and the results show that the proposed method that consider
    persistency in volatility gives better performance and the accuracy error is six time smaller
    than the normal hybrid model.
    Matched MeSH terms: Forecasting
  5. Fulltext Inventory management improvement suggestion through time-series forecasting for financial service company
    Adipriyana, Raditianto, Rosmahaida Jamaludin, Hayati Habibah Abdul Talib
    Journal of Advanced Research Design, 2018;49(1):7-14.
    MyJurnal
    Management is consistently facing fast-flowing and lots of changes in business, including in the inventory management. Especially for fast-moving inventories, the correct stocking, controlling, checking and safety stock calculation is highly needed to have an exquisite inventory management and to reduce the possibility of running out of inventory which leads to unavailability to meet the demand. One of the ways to overcome this is by doing an excellent and appropriate forecasting. Therefore, the objective of this concept paper is to analyse and recommend tools to improve inventory management using the appropriate time-series forecasting method. The firm studied in this study is serving its employees as customers that demand the routine items including stationeries and other routine products to support their job as auditors and consultants for its client. However, there are occasions when there is out-of-stock situation for fast-moving items, especially in the peak season period. Furthermore, the firm is only applying replenishment based on the used inventories from the previous month. Therefore, this study suggests to eliminate out-of-stock items situation by applying precaution initiatives such as time-series forecasting. This study is planned to employ 10 time-series forecasting methods such as moving average, exponential smoothing, regression analysis, Holt-Winters analysis, Seasonal analysis and Autoregressive Integrated Moving Average (ARIMA) using Risk Simulator Software. By simulating those methods, the most appropriate method is selected based on the forecasting accuracy measurement.
    Matched MeSH terms: Forecasting
  6. Comparing the accuracy of density forecasts from competing GARCH models
    Abu Hassan Shaari Mohd Nor, Ahmad Shamiri, Zaidi Isa
    Sains Malaysiana, 2009;38.
    In this research we introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. Use of KLIC is practically attractive as well as convenient, given its equivalent of the widely used LR test. We include an illustrative simulation to compare a set of distributions, including symmetric and asymmetric distribution, and a family of GARCH volatility models. Our results on simulated data show that the choice of the conditional distribution appears to be a more dominant factor in determining the adequacy and accuracy (quality) of density forecasts than the choice of volatility model.
    Matched MeSH terms: Forecasting
  7. Projection of storm surge climate extreme over Sunda Shelf based on IPCC SRES A2 scenario
    Wah SH, Halimatun Muhamad, Tangang FT, Liew J
    Sains Malaysiana, 2012;41:1411-1422.
    The historical and future storm surge climate over the South China Sea Sunda Shelf was derived using a barotropic two dimensional model. The atmospheric forcings were obtained from the UKMO regional climate modeling system, PRECIS (Providing Regional Climates for Impacts Studies), forced at the boundary by the ECHAM4 simulation output under the SRES A2 emission experiment. In general, the model simulates historical sea surface elevation characteristics satisfactory although there is a substantial underestimation for the sea level elevation at local scales. The climate change analysis suggests that the storm surge extreme over the Sunda Shelf is expected to increase along the coastal area of the Gulf of Thailand and east coast of Peninsular Malaysia in the future (2071-2100). The projected increment is averagely ~9% over the Sunda Shelf region by the end of the 21st century corresponding to about 5% stronger wind speed as compare to the baseline period of 1961-1990.
    Matched MeSH terms: Forecasting
  8. Asymmetry dynamic volatility forecast evaluations using interday and intraday data
    Chin WC, Chin WC, Zaidi Isa, Abu Hassan Shaari Mohd Nor
    Sains Malaysiana, 2012;41:1287-1299.
    The accuracy of financial time series forecasts often rely on the model precision and the availability of actual observations for forecast evaluations. This study aimed to tackle these issues in order to obtain a suitable asymmetric time-varying volatility model that outperformed in the forecast evaluations based on interday and intraday data. The model precision was examined based on the most appropriate time-varying volatility representation under the autoregressive conditional heteroscedascity framework. For forecast precision, the evaluations were conducted under three loss functions using the volatility proxies and realized volatility. The empirical studies were implemented on two major financial markets and the estimated results are applied in quantifying their market risks. Empirical results indicated that Zakoian model provided the best in-sample forecasts whereas DGE on the other hand indicated better out-of-sample forecasts. For the type of volatility proxy selection, the implementation of intraday data in the latent volatility indicated significant improvement in all the time horizon forecasts.
    Matched MeSH terms: Forecasting
  9. Time series analysis of surface ozone monitoring records in Kemaman, Malaysia
    Marzuki Ismail, Mohd Zamri Ibrahim, Tg. Azmina Ibrahim, Ahmad Makmon Abdullah
    Sains Malaysiana, 2011;40:1179-1186.
    Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). In this study we used Box-Jenkins methodology to build ARIMA model for monthly ozone data taken from an Automatic Air Quality Monitoring System in Kemaman station for the period from 1996 to 2007 with a total of 144 readings. Parametric seasonally adjusted ARIMA (0,1,1) (1,1,2)12 model was successfully applied to predict the long-term trend of ozone concentration. The detection of a steady statistical significant upward trend for ozone concentration in Kemaman is quite alarming. This is likely due to sources of ozone precursors related to industrial activities from nearby areas and the increase in road traffic volume.
    Matched MeSH terms: Forecasting
  10. On the relationship between inflation rate and inflation uncertainty: an application of the GARCH family models
    Abu Hassan Shaari Mohd Nor, Tan YL, Fauziah Maarof
    Sains Malaysiana, 2007;36:225-232.
    The main objective of this paper is to explore the varying volatility dynamic of inflation rate in Malaysia for the period from January 1980 to December 2004. The GARCH, GARCH-Mean, EGARCH and EGARCH-Mean models are used to capture the stochastic variation and asymmetries in the economic instruments. Results show that the EGARCH model gives better estimates of sub-periods volatility. Further analysis using Granger causality test show that there is sufficient empirical evidence that higher inflation rate level will result in higher future inflation uncertainty and higher level of inflation uncertainty will lead to lower future inflation rate.
    Matched MeSH terms: Forecasting
  11. Point forecast Markov Switching model for U.S. Dollar/ Euro exchange rate
    MOSTAFAEI H, Safaei M
    Sains Malaysiana, 2012;41:481-488.
    This research proposes a point forecasting method into Markov switching autoregressive model. In case of two regimes, we proved the probability that h periods later process will be in regime 1 or 2 is given by steady-state probabilities. Then, using the value of h-step-ahead forecast data at time t in each regime and using steady-state probabilities, we present an h-step-ahead point forecast of data. An empirical application of this forecasting technique for U.S. Dollar/ Euro exchange rate showed that Markov switching autoregressive model achieved superior forecasts relative to the random walk with drift. The results of out-of-sample forecast indicate that the fluctuations of U.S. Dollar/ Euro exchange rate from May 2011 to May 2013 will be rising.
    Matched MeSH terms: Forecasting
  12. Fulltext Landslide factors and susceptibility mapping on natural and artificial slopes in Kundasang, Sabah
    Sharir K, Roslee R, Lee KE, Simon N
    Sains Malaysiana, 2017;46:1521-1540.
    This study was carried out on the hilly topographic area in Kundasang, Sabah. This area is known to be extremely prone to landslides that occurred either naturally or by human interference to natural slopes. Aerial photographs interpretation was conducted in order to identify landslide distributions across three assessment years (2012, 2009 and 1984). These datasets were classified into two landslides groups based on their occurrences; natural and artificial. A total of 362 naturally occurring landslides were identified and another 133 are artificial slope landslides. Physical parameters which include lithology, slope angle, slope aspect and soil series were analyzed with each landslide group to examine the different influence of these parameters on each of the group. From the analysis, the landslide density for the natural landslide group shows that more than 35° slope angle and slope aspect facing east and southwest are prone to landslides. In terms of geological materials, high landslide density is recorded in the phyllite, shale, siltstone and sandstone lithologies group and the Pinosuk, Kepayan and Trusmadi soil series. In contrast, for the artificial
    slope landslide, high landslide density is observed in the 25°-35° slope angle and similar density in every slope aspect classes. The geological materials however have similar landslide density across their factors’ classes. The landslide density technique was also used to generate the landslide susceptibility maps for both landslide conditions. Validation of the maps shows acceptable accuracy of 71% and 74%, respectively, for both natural and artificial slope landslide susceptibility maps and this shows that these maps can be used for future land use planning.
    Matched MeSH terms: Forecasting
  13. Load forecasting using combination model of multiple linear regression with neural network for Malaysian city
    Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono Suhartono, Abdul Ghapor Hussin, Yong Zulina Zubairi
    Sains Malaysiana, 2018;47:419-426.
    Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
    data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
    forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
    relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
    model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
    combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
    of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
    error graphically. From the result obtained this model gives a better forecast compare to the other two models.
    Matched MeSH terms: Forecasting
  14. The HARX-GJR-GARCH skewed-t multipower realized volatility modelling for S&P 500
    Chin WC, Nadira Mohamed Isa, Nadira Mohamed Isa, Lee MC, Poo KH
    Sains Malaysiana, 2017;46:107-116.
    The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the
    S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have
    the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate
    clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold
    autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized
    volatility are characterized by the skewed student-t distributions. The extended model provides the best performing insample
    and out-of-sample forecast evaluations.
    Matched MeSH terms: Forecasting
  15. Experimental study on propagation and attenuation regularity oflandslide surge
    Fuxing Zu, Pingyi Wang, Jiqing Xu, Liquan Xie
    Sains Malaysiana, 2017;46:2061-2074.
    On the basis of landslide surge model test by adopting generalized simulation of waterways, this paper, for the first time, established a four-dimensional mathematical model between wave height transmissibility rate and the initial wave height, water depth, azimuth angle as well as propagation distance through utilizing the method of tensor space mapping. Using the new model, we proposed an empirical wave field covering all areas of the channel including the attenuation area within the width of a landslide mass, the straight channel attenuation area outside the width of the landslide mass, the curved channel attenuation area and the after-curve attenuation area, which comprehensively reflects the progressive changes of surge wave factors. The transmissibility of wave height and propagation distance are in a bivariate negative exponential distribution, and the wave height gradually reduces and the attenuation also slows down as the propagation distance increases; wave height transmissibility rate, azimuth and propagation distance are in a trivariate negative exponential distribution, the attenuation of the wave height in the straight channel within the width of the landslide mass was the slowest, followed by that of wave in the straight channel outside the width of the landslide mass, and the attenuation of the wave height in the curved channel is the greatest. This empirical wave field was based on test data, scientifically abstracted the general regularity of the propagation and attenuation of landslide surge, which can be applied to similar analyses and forecasts on landslide surge and can scientifically and accurately determine the damage range of landslide surge.
    Matched MeSH terms: Forecasting
  16. Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend
    Pazikadin AR, Rifai D, Ali K, Malik MZ, Abdalla AN, Faraj MA
    Sci Total Environ, 2020 May 01;715:136848.
    PMID: 32040994 DOI: 10.1016/j.scitotenv.2020.136848
    The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.
    Matched MeSH terms: Forecasting
  17. Optimised and systematic suitable climate modelling confirms future longitudinal-trends for growing oil palm in Africa
    Paterson RRM
    J Environ Manage, 2021 Dec 15;300:113785.
    PMID: 34562818 DOI: 10.1016/j.jenvman.2021.113785
    Palms are iconic plants. Oil palms are very important economically and originate in Africa where they can act as a model for palms in general. The effect of future climate on the growth of oil palm will be very detrimental. Latitudinal migration of tropical crops to climate refuges may be impossible, and longitudinal migration has only been confirmed for oil palm, of all the tropical crops. The previous method to determine the longitudinal trend for oil palm used the longitudes of various countries in Africa and plotted these against the percentage suitable climate for growing oil palms in each country. An increasing longitudinal trend was observed from west to east. However, the longitudes of the countries were randomly distributed which may have introduced bias and the procedure was time consuming. The present report presents an optimised and systematic procedure that divided the regions, as presented on a map derived from a CLIMEX model, into ten equal sectors and the percentage suitable climates for growing oil palm were determined for each sector. This approach was quicker, systematic and straight forward and will be useful for management of oil palm plantations under climate change. The method confirmed and validated the trends reported in the original method although the suitability values were often lower and there was less spread of values around the trend. The values for the CSIRO MK3.0 and MIROC H models demonstrated considerable similarities to each other, contributing to validation of the method. The procedure of dividing maps equally into sectors derived from models, could be used for other crops, regions, or systems more generally, where the alternative may be a more superficial visual examination of the maps. Methods are required to mitigate the effects of climate change and stakeholders need to contribute more actively to the current climate debate with tangible actions.
    Matched MeSH terms: Forecasting
  18. Fulltext Data-driven models for atmospheric air temperature forecasting at a continental climate region
    Alomar MK, Khaleel F, Aljumaily MM, Masood A, Razali SFM, AlSaadi MA, et al.
    PLoS One, 2022;17(11):e0277079.
    PMID: 36327280 DOI: 10.1371/journal.pone.0277079
    Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.
    Matched MeSH terms: Forecasting
  19. Fulltext The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review
    Baharom M, Ahmad N, Hod R, Arsad FS, Tangang F
    Int J Environ Res Public Health, 2021 10 22;18(21).
    PMID: 34769638 DOI: 10.3390/ijerph182111117
    BACKGROUND: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming.

    METHOD: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies.

    RESULTS: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses.

    CONCLUSION: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.

    Matched MeSH terms: Forecasting
  20. Fulltext PM2.5 forecasting for an urban area based on deep learning and decomposition method
    Zaini N, Ean LW, Ahmed AN, Abdul Malek M, Chow MF
    Sci Rep, 2022 Oct 20;12(1):17565.
    PMID: 36266317 DOI: 10.1038/s41598-022-21769-1
    Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.
    Matched MeSH terms: Forecasting
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