Displaying publications 1 - 20 of 252 in total

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  1. Formation, modeling and validation of trihalomethanes (THM) in Malaysian drinking water: a case study in the districts of Tampin, Negeri Sembilan and Sabak Bernam, Selangor, Malaysia
    Abdullah MP, Yew CH, Ramli MS
    Water Res, 2003 Nov;37(19):4637-44.
    PMID: 14568050
    A modeling procedure that predicts trihalomethane (THM) formation from field sampling at the treatment plant and along its distribution system using Tampin district, Negeri Sembilan and Sabak Bernam district, Selangor as sources of data were studied and developed. Using Pearson method of correlation, the organic matter measured as TOC showed a positive correlation with formation of THM (r=0.380,P=0.0001 for Tampin and r=0.478,P=0.0001 for Sabak Bernam). Similar positive correlation was also obtained for pH in both districts with Tampin (r=0.362,P=0.0010) and Sabak Bernam (r=0.215,P=0.0010). Chlorine dosage was also found to have low correlation with formation of THM for the two districts with Tampin (r=0.233,P=0.0230) and Sabak Bernam (r=0.505,P=0.0001). Distance from treatment plant was found to have correlation with formation of THM for Tampin district with r=0.353 and P=0.0010. Other parameters such as turbidity, ammonia, temperature and residue chlorine were found to have no correlation with formation of THM. Linear and non-linear models were developed for these two districts. The results obtained were validated using three different sets of field data obtained from own source and district of Seremban (Pantai and Sg. Terip), Negeri Sembilan. Validation results indicated that there was significant difference in the predictive and determined values of THM when two sets of data from districts of Seremban were used with an exception of field data of Sg. Terip for non-linear model developed for district of Tampin. It was found that a non-linear model is slightly better than linear model in terms of percentage prediction errors. The models developed were site specific and the predictive capabilities in the distribution systems vary with different environmental conditions.
    Matched MeSH terms: Forecasting
  2. Telemedicine in the Malaysian Multimedia Super Corridor: towards personalized lifetime health plans
    Abidi SS, Yusoff Z
    Stud Health Technol Inform, 1999;68:283-8.
    PMID: 10724889
    The Malaysian Telemedicine initiative advocates a paradigm shift in healthcare delivery patterns by way of implementing a person-centred and wellness-focused healthcare system. This paper introduces the Malaysian Telemedicine vision, its functionality and associated operational conditions. In particular, we focus on the conceptualisation of one key Telemedicine component i.e. the Lifetime Health Plan (LHP) system--a distributed multimodule application for the periodic monitoring and generation of health-care advisories for all Malaysians. In line with the LHP project, we present an innovative healthcare delivery info-structure--LifePlan--that aims to provide life-long, pro-active, personalised, wellness-oriented healthcare services to assist individuals to manage and interpret their health needs. Functionally, LifePlan based healthcare services are delivered over the WWW, packaged as Personalised Lifetime Health Plans that allow individuals to both monitor their health status and to guide them in healthcare planning.
    Matched MeSH terms: Forecasting
  3. Projecting social support needs of informal caregivers in Malaysia
    Abu Bakar SH, Weatherley R, Omar N, Abdullah F, Mohamad Aun NS
    Health Soc Care Community, 2014 Mar;22(2):144-54.
    PMID: 24024495 DOI: 10.1111/hsc.12070
    This article presents the findings of a self-report study of the consequences of being an informal caregiver in Malaysia. The aim of this exploratory study was to examine Malaysian efforts in assisting informal caregivers, based on an analysis of the issues and concerns raised by the caregivers themselves. Data were obtained from a cross-sectional survey of informal caregivers in 2009. This sample comprised parents, spouses and/or adult siblings, and adult children, caring for their children, spouses or siblings and parents who were chronically ill and/or had a disability. Of 300 prospective participants, only 175 could be located (58%), but all those contacted agreed to participate. Respondents were randomly selected and interviewed using a structured questionnaire to identify the emotional, financial, social and physical issues consequent upon being a caregiver. Most respondents reported that their care-giving responsibilities had impacted their emotional, financial, social and/or physical well-being. Inadequate and/or uncertain income was by far the greatest concern followed in descending order by social, physical and emotional consequences. The one-way analysis of variance showed significant differences among the three categories of caregivers with respect to physical and emotional consequences. The findings show that care-giving has detrimental effects on the lives of informal caregivers, and that they are in significant need of social support to help them deal with care-giving tasks and responsibilities. Based on the findings, an integrated social support programme is proposed, tailored to the needs of informal caregivers.
    Matched MeSH terms: Forecasting
  4. 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
  5. 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
  6. Regional landfills methane emission inventory in Malaysia
    Abushammala MF, Noor Ezlin Ahmad Basri, Basri H, Ahmed Hussein El-Shafie, Kadhum AA
    Waste Manag Res, 2011 Aug;29(8):863-73.
    PMID: 20858637 DOI: 10.1177/0734242X10382064
    The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50-60% methane (CH(4)) and 30-40% carbon dioxide (CO(2)) by volume. CH(4) has a global warming potential 21 times greater than CO(2); thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH(4) emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH(4) that would be emitted from landfills in the period from 1981-2024 using the IPCC 2006 FOD model. The total CH(4) emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH(4) emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH(4) emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH( 4) emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH(4) emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.
    Matched MeSH terms: Forecasting
  7. Fulltext Projected Streamflow in the Kurau River Basin of Western Malaysia under Future Climate Scenarios
    Adib MNM, Rowshon MK, Mojid MA, Habibu I
    Sci Rep, 2020 05 20;10(1):8336.
    PMID: 32433561 DOI: 10.1038/s41598-020-65114-w
    Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021-2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and -3.0, respectively for calibration period (1981-1998) and 0.60, 0.59 and -4.6, respectively for validation period (1996-2005). Future projections over 2021-2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5 °C by 2021-2080 relative to baseline period of 1976-2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021-2050 period, the projected streamflow will be higher during 2051-2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between -2.8% and -4.3% during the off season, and between 0% (nil) and -3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.
    Matched MeSH terms: Forecasting
  8. 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
  9. Fulltext Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting
    Afan HA, Allawi MF, El-Shafie A, Yaseen ZM, Ahmed AN, Malek MA, et al.
    Sci Rep, 2020 03 13;10(1):4684.
    PMID: 32170078 DOI: 10.1038/s41598-020-61355-x
    In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.
    Matched MeSH terms: Forecasting
  10. Educating medical students in oral health care: current curriculum and future needs of institutions in Malaysia and Australia
    Ahmad MS, Abuzar MA, Razak IA, Rahman SA, Borromeo GL
    Eur J Dent Educ, 2017 Nov;21(4):e29-e38.
    PMID: 27273317 DOI: 10.1111/eje.12211
    Poor oral health has been associated with compromised general health and quality of life. To promote comprehensive patient management, the role of medical professionals in oral health maintenance is compelling, thus indicating the need for educational preparation in this area of practice. This study aimed to determine the extent of training in oral health in Malaysian and Australian medical schools. An audio-recorded semi-structured phone interview involving Academic Programme Directors in Malaysian (n = 9, response rate=81.8%) and Australian (n = 7, response rate = 35.0%) medical schools was conducted during the 2014/2015 and 2014 academic years, respectively. Qualitative data was analysed via thematic analysis, involving coding and grouping into emerging themes. Quantitative data were measured for frequencies. It was found that medical schools in Malaysia and Australia offered limited teaching of various oral health-related components that were mostly integrated throughout the curriculum, in the absence of structured learning objectives, teaching methodologies and assessment approaches. Barriers to providing oral health education included having insufficient expertise and overloaded curriculum. As medical educators demonstrated support for oral health education, collaboration amongst various stakeholders is integral to developing a well-structured curriculum and practice guidelines on oral health management involving medical professionals.
    Matched MeSH terms: Forecasting
  11. Implications of climate change damage for agriculture: sectoral evidence from Pakistan
    Ahmed A, Devadason ES, Al-Amin AQ
    Environ Sci Pollut Res Int, 2016 Oct;23(20):20688-20699.
    PMID: 27473615
    This paper gives a projection of the possible damage of climate change on the agriculture sector of Pakistan for the period 2012-2037, based on a dynamic approach, using an environment-related applied computable general equilibrium model (CGE). Climate damage projections depict an upward trend for the period of review and are found to be higher than the global average. Further, the damage to the agricultural sector exceeds that for the overall economy. By sector, climatic damage disproportionately affects the major and minor crops, livestock and fisheries. The largest losses following climate change, relative to the other agricultural sectors, are expected for livestock. The reason for this is the orthodox system of production for livestock, with a low adaptability to negative shocks of climate change. Overall, the findings reveal the high exposure of the agriculture sector to climate damage. In this regard, policymakers in Pakistan should take seriously the effects of climate change on agriculture and consider suitable technology to mitigate those damages.
    Matched MeSH terms: Forecasting
  12. Fulltext Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review
    Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al.
    Biomed Res Int, 2021;2021:9751564.
    PMID: 34258283 DOI: 10.1155/2021/9751564
    Objective: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.

    Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.

    Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.

    Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

    Matched MeSH terms: Forecasting
  13. Fulltext Antiviral and Immunomodulatory Effects of Phytochemicals from Honey against COVID-19: Potential Mechanisms of Action and Future Directions
    Al-Hatamleh MAI, Hatmal MM, Sattar K, Ahmad S, Mustafa MZ, Bittencourt MC, et al.
    Molecules, 2020 Oct 29;25(21).
    PMID: 33138197 DOI: 10.3390/molecules25215017
    The new coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has recently put the world under stress, resulting in a global pandemic. Currently, there are no approved treatments or vaccines, and this severe respiratory illness has cost many lives. Despite the established antimicrobial and immune-boosting potency described for honey, to date there is still a lack of evidence about its potential role amid COVID-19 outbreak. Based on the previously explored antiviral effects and phytochemical components of honey, we review here evidence for its role as a potentially effective natural product against COVID-19. Although some bioactive compounds in honey have shown potential antiviral effects (i.e., methylglyoxal, chrysin, caffeic acid, galangin and hesperidinin) or enhancing antiviral immune responses (i.e., levan and ascorbic acid), the mechanisms of action for these compounds are still ambiguous. To the best of our knowledge, this is the first work exclusively summarizing all these bioactive compounds with their probable mechanisms of action as antiviral agents, specifically against SARS-CoV-2.
    Matched MeSH terms: Forecasting
  14. Fulltext Predicting physical time series using dynamic ridge polynomial neural networks
    Al-Jumeily D, Ghazali R, Hussain A
    PLoS One, 2014;9(8):e105766.
    PMID: 25157950 DOI: 10.1371/journal.pone.0105766
    Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.
    Matched MeSH terms: Forecasting
  15. Fulltext Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting
    Alenezy AH, Ismail MT, Jaber JJ, Wadi SA, Alkhawaldeh RS
    PLoS One, 2022;17(12):e0278835.
    PMID: 36490280 DOI: 10.1371/journal.pone.0278835
    This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia's stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzzy inference rules. The inputs are the oil price (Loil) and repo rate (Repo) according to multiple regression correlation, and the Engle and Granger Causality test Engle RF, (1987). The logarithm of the stock market price (LSCS) in Tadawul reflects the output variable. The correlation matrix reveals that there is no collinearity between the input variables, and the causality test demonstrates that the input variables significantly influence the outcome variable. According to the multiple regression, there is a substantial negative influence between Loil and LSCS but a significant positive effect between Repo and output. For the 80% dataset under ME (0.000005), MAE (0.003214), and MAPE (0.064497), the MODWT-LA8 (ARIMA(1,1,0) with drift) for the LSCS variable performs better than other WT functions. In the novel hybrid model MODWT-FIR.DM, each function's approximation coefficient (LSCS) is applied with input variables (Loil and Repo). We evaluate the performance of the proposed model (MODWT-LA8-FIR.DM) using different statistical measures (ME, RMSE, MAE, MPE) and compare it to two established models: the original FIR.DM and other MODWT-FIR.DM functions for forecasting 20% of datasets. The outcomes show that the MODWT-LA8-FIR.DM performs better than the traditional models based on lower ME (3.167586), RMSE (3.167638), MAE (3.167586), and MPE (80.860849). The proposed hybrid model may be a potential stock market forecasting model.
    Matched MeSH terms: Forecasting
  16. A Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting
    Alfa Mohammed Salisu, Ani Shabri
    MATEMATIKA, 2020;36(2):141-156.
    MyJurnal
    This paper proposes A Hybrid Wavelet-Auto-Regressive Integrated Moving Average (W-ARIMA) model to explore the ability of the hybrid model over an ARIMA model. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMA model using the Standardized Precipitation Index (SPI) drought data for forecasting drought modeling development. SPI data from January 1954 to December 2008 used was divided into two - (80%/20% for training/testing respectively). The results were compared with the conventional ARIMA model with Mean Square Error (MSE) and Mean Average Error (MAE) as an error measure. The results of the proposed method achieved the best forecasting performance.
    Matched MeSH terms: Forecasting
  17. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models
    Allawi MF, Jaafar O, Mohamad Hamzah F, Abdullah SMS, El-Shafie A
    Environ Sci Pollut Res Int, 2018 May;25(14):13446-13469.
    PMID: 29616480 DOI: 10.1007/s11356-018-1867-8
    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.
    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 Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020
    Alsayed A, Sadir H, Kamil R, Sari H
    Int J Environ Res Public Health, 2020 Jun 08;17(11).
    PMID: 32521641 DOI: 10.3390/ijerph17114076
    The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible-Exposed-Infectious-Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July-11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.
    Matched MeSH terms: Forecasting
  20. Fulltext Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators
    Alwee R, Shamsuddin SM, Sallehuddin R
    ScientificWorldJournal, 2013;2013:951475.
    PMID: 23766729 DOI: 10.1155/2013/951475
    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
    Matched MeSH terms: Forecasting*
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