Displaying publications 1 - 20 of 252 in total

Abstract:
Sort:
  1. A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
    Chun TS, Malek MA, Ismail AR
    Water Sci Technol, 2015;71(4):524-8.
    PMID: 25746643 DOI: 10.2166/wst.2014.451
    The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
    Matched MeSH terms: Forecasting
  2. A clonal selection algorithm model for daily rainfall data prediction
    Noor Rodi NS, Malek MA, Ismail AR, Ting SC, Tang CW
    Water Sci Technol, 2014;70(10):1641-7.
    PMID: 25429452 DOI: 10.2166/wst.2014.420
    This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.
    Matched MeSH terms: Forecasting/methods*
  3. Comparison of field measurements to predicted reaeration coefficients, k2, in the application of a water quality model, QUAL2E, to a tropical river
    Mohamed M, Stednick JD, Smith FM
    Water Sci Technol, 2002;46(9):47-54.
    PMID: 12448451
    Some of the many tools used for watershed management are mathematical and computer models for wasteload allocations. QUAL2E is one of the most popular water quality models used for such purposes. The question arises as to whether the model is applicable in a different climate such as that in the tropics. In this study, QUAL2E was used to model Sg. Selangor River in Malaysia using the predictive equations for reaeration coefficient (k2) within the model and the measured reaeration coefficients for the river. The study results indicated that use of the reaeration coefficient (k2) measured at Sg. Selangor River did give the lowest standard error (SE) for the simulation of water quality during the 7Q10 low-flow period which is considered as the worst scene scenario in water quality modeling. But during calibration and validation using actual low-flow discharge data, the measured reaeration coefficients did not give the lowest standard error (SE). In conclusion, the results indicated that QUAL2E is applicable in tropical rivers when used with the modeled river parameters (i.e. hydraulic parameters, meteorological conditions etc.). Measured reaeration coefficients produced good results and several predictive equations also produced comparatively good results.
    Matched MeSH terms: Forecasting
  4. Water and wastewater in developing countries: present reality and strategy for the future
    Ujang Z, Buckley C
    Water Sci Technol, 2002;46(9):1-9.
    PMID: 12448446
    This paper summarises the paper presentation sessions at the Conference, as well giving insights on the issues related to developing countries. It also discusses the present status of practice and research on water and wastewater management, and projected future scenario based not only on the papers presented in the Conference, but also on other sources. The strategy is presented to overcome many problems in developing countries such as rapid urbanization, industrialization, population growth, financial and institutional problems and, depleting water resources. The strategy consists of Integrated Urban Water Management (IUWM), cleaner industrial production, waste minimisation and financial arrangements.
    Matched MeSH terms: Forecasting
  5. Environmental engineering education for developing countries: framework for the future
    Ujang Z, Henze M, Curtis T, Schertenleib R, Beal LL
    Water Sci Technol, 2004;49(8):1-10.
    PMID: 15193088
    This paper presents the existing philosophy, approach, criteria and delivery of environmental engineering education (E3) for developing countries. In general, environmental engineering is being taught in almost all major universities in developing countries, mostly under civil engineering degree programmes. There is an urgent need to address specific inputs that are particularly important for developing countries with respect to the reality of urbanisation and industrialisation. The main component of E3 in the near future will remain on basic sanitation in most developing countries, with special emphasis on the consumer-demand approach. In order to substantially overcome environmental problems in developing countries, E3 should include integrated urban water management, sustainable sanitation, appropriate technology, cleaner production, wastewater minimisation and financial framework.
    Matched MeSH terms: Forecasting*
  6. 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
  7. Groundwater level forecasting with machine learning models: A review
    Boo KBW, El-Shafie A, Othman F, Khan MMH, Birima AH, Ahmed AN
    Water Res, 2024 Mar 15;252:121249.
    PMID: 38330715 DOI: 10.1016/j.watres.2024.121249
    Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
    Matched MeSH terms: Forecasting
  8. Assessment of municipal solid waste generation and recyclable materials potential in Kuala Lumpur, Malaysia
    Saeed MO, Hassan MN, Mujeebu MA
    Waste Manag, 2009 Jul;29(7):2209-13.
    PMID: 19369061 DOI: 10.1016/j.wasman.2009.02.017
    This paper presents a forecasting study of municipal solid waste generation (MSWG) rate and potential of its recyclable components in Kuala Lumpur (KL), the capital city of Malaysia. The generation rates and composition of solid wastes of various classes such as street cleansing, landscape and garden, industrial and constructional, institutional, residential and commercial are analyzed. The past and present trends are studied and extrapolated for the coming years using Microsoft office 2003 Excel spreadsheet assuming a linear behavior. The study shows that increased solid waste generation of KL is alarming. For instance, the amount of daily residential SWG is found to be about 1.62 kg/capita; with the national average at 0.8-0.9 kg/capita and is expected to be increasing linearly, reaching to 2.23 kg/capita by 2024. This figure seems reasonable for an urban developing area like KL city. It is also found that, food (organic) waste is the major recyclable component followed by mix paper and mix plastics. Along with estimated population growth and their business activities, it has been observed that the city is still lacking in terms of efficient waste treatment technology, sufficient fund, public awareness, maintaining the established norms of industrial waste treatment etc. Hence it is recommended that the concerned authority (DBKL) shall view this issue seriously.
    Matched MeSH terms: Forecasting
  9. Municipal solid waste management in Malaysia: practices and challenges
    Manaf LA, Samah MA, Zukki NI
    Waste Manag, 2009 Nov;29(11):2902-6.
    PMID: 19540745 DOI: 10.1016/j.wasman.2008.07.015
    Rapid economic development and population growth, inadequate infrastructure and expertise, and land scarcity make the management of municipal solid waste become one of Malaysia's most critical environmental issues. The study is aimed at evaluating the generation, characteristics, and management of solid waste in Malaysia based on published information. In general, the per capita generation rate is about 0.5-0.8 kg/person/day in which domestic waste is the primary source. Currently, solid waste is managed by the Ministry of Housing and Local Government, with the participation of the private sector. A new institutional and legislation framework has been structured with the objectives to establish a holistic, integrated, and cost-effective solid waste management system, with an emphasis on environmental protection and public health. Therefore, the hierarchy of solid waste management has given the highest priority to source reduction through 3R, intermediate treatment and final disposal.
    Matched MeSH terms: Forecasting
  10. Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model
    Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, Younes MY
    Waste Manag, 2016 Sep;55:3-11.
    PMID: 26522806 DOI: 10.1016/j.wasman.2015.10.020
    Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.
    Matched MeSH terms: Forecasting
  11. Trends in sustainable landfilling in Malaysia, a developing country
    Fauziah SH, Agamuthu P
    Waste Manag Res, 2012 Jul;30(7):656-63.
    PMID: 22455994 DOI: 10.1177/0734242X12437564
    In Malaysia, landfills are being filled up rapidly due to the current daily generation of approximately 30,000 tonnes of municipal solid waste. This situation creates the crucial need for improved landfilling practices, as sustainable landfilling technology is yet to be achieved here. The objective of this paper is to identify and evaluate the development and trends in landfilling practices in Malaysia. In 1970, the disposal sites in Malaysia were small and prevailing waste disposal practices was mere open-dumping. This network of relatively small dumps, typically located close to population centres, was considered acceptable for a relatively low population of 10 million in Malaysia. In the 1980s, a national programme was developed to manage municipal and industrial wastes more systematically and to reduce adverse environmental impacts. The early 1990s saw the privatization of waste management in many parts of Malaysia, and the establishment of the first sanitary landfills for MSW and an engineered landfill (called 'secure landfill' in Malaysia) for hazardous waste. A public uproar in 2007 due to contamination of a drinking water source from improper landfilling practices led to some significant changes in the government's policy regarding the country's waste management strategy. Parliament passed the Solid Waste and Public Cleansing Management (SWPCM) Act 2007 in August 2007. Even though the Act is yet to be implemented, the government has taken big steps to improve waste management system further. The future of the waste management in Malaysia seems somewhat brighter with a clear waste management policy in place. There is now a foundation upon which to build a sound and sustainble waste management and disposal system in Malaysia.
    Matched MeSH terms: Forecasting
  12. 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
  13. Mortality in Asia
    WHO Chron, 1981;35(5):163-7.
    PMID: 7324457
    Matched MeSH terms: Forecasting
  14. Revisiting Roles of Health Technology Assessment on Drug Policy in Universal Health Coverage in Thailand: Where Are We? And What Is Next?
    Tanvejsilp P, Taychakhoonavudh S, Chaikledkaew U, Chaiyakunapruk N, Ngorsuraches S
    Value Health Reg Issues, 2019 May;18:78-82.
    PMID: 30641410 DOI: 10.1016/j.vhri.2018.11.004
    OBJECTIVES: To describe the process, challenges, and future direction of health technology assessment (HTA), focusing on the drug selection of the National List of Essential Medicines (NLEM) in Thailand.

    METHODS: Literature and government documents were reviewed and analyzed by authors with experiences in HTA and drug policy in the country.

    RESULTS: The structure of HTA and its process in the drug selection of the NLEM were described, followed by the outcomes of the use of HTA. Examples of lowering drug prices, as a result of price negotiation using HTA, were presented. A few examples were also provided to demonstrate how decisions were made from considering factors beyond cost-effectiveness findings. Finally, challenges on various issues including improvement of HTA structure and process were discussed for the future direction of HTA in Thailand.

    CONCLUSIONS: HTA has been adopted as a tool for the drug selection of the NLEM to help Thailand achieve universal health coverage. Nevertheless, various challenges exist and need to be addressed.

    Matched MeSH terms: Forecasting
  15. Shear Wave Elastography: A Review on the Confounding Factors and Their Potential Mitigation in Detecting Chronic Kidney Disease
    Lim WTH, Ooi EH, Foo JJ, Ng KH, Wong JHD, Leong SS
    Ultrasound Med Biol, 2021 08;47(8):2033-2047.
    PMID: 33958257 DOI: 10.1016/j.ultrasmedbio.2021.03.030
    Early detection of chronic kidney disease is important to prevent progression of irreversible kidney damage, reducing the need for renal transplantation. Shear wave elastography is ideal as a quantitative imaging modality to detect chronic kidney disease because of its non-invasive nature, low cost and portability, making it highly accessible. However, the complexity of the kidney architecture and its tissue properties give rise to various confounding factors that affect the reliability of shear wave elastography in detecting chronic kidney disease, thus limiting its application to clinical trials. The objective of this review is to highlight the confounding factors presented by the complex properties of the kidney, in addition to outlining potential mitigation strategies, along with the prospect of increasing the versatility and reliability of shear wave elastography in detecting chronic kidney disease.
    Matched MeSH terms: Forecasting
  16. Fulltext RAINFALL FORECASTING WITH TIME SERIES MODEL IN ALOR SETAR, KEDAH
    FAIQAH MOHAMAD FUDZI, ZAHAYU MD YUSOF, MASNITA MISIRAN
    UMT Journal of Undergraduate Research, 2021;3(1):37-44.
    MyJurnal
    The prediction of rainfall on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. In this paper, the study is conducted to examine the pattern of monthly rainfall in Alor Setar, Kedah within ten years which is from 2008 to 2018. This paper considered a model based on real data that obtained from Department of Meteorology Malaysia. This study indicates that the monthly rainfall in Alor Setar has a seasonal and trend pattern based on yt vs t plotting, autocorrelation function and Kruskal Wallis Test for seasonality. The examined rainfall time-series modelling approaches include Naïve Model, Decomposition Method, Holt-Winter’s and Box-Jenkins ARIMA. Multiplicative Decomposition Method was identified as the best model to forecast rainfall for the year of 2019 by analysing the previous ten-year’s data (2008-2018).As a result from the forecast of 2019, October is the wettest month with highest forecasted rainfall of 276.15mm while the driest month is in February with lowest forecasted rainfall of 50.55mm. The model is therefore adequate and appropriate to forecast future monthly rainfall values in the catchment which can help farmers to plan their farming activities ahead of time.
    Matched MeSH terms: Forecasting
  17. Fulltext GOLD PRICE FORECASTING USING BOX-JENKINS METHOD
    NUR ATIKAH KHALID, NURFADHLINA ABDUL HALIM
    UMT Journal of Undergraduate Research, 2019;1(3):15-27.
    MyJurnal
    In general, the nature of gold that acts as a hedge against inflation and its stable price over the course of the financial crisis has made it a unique commodity. Priceforecasts are a must for gold producers, investors and central bank to know the current trends in gold prices. Forecasting the future value of a variableis often done with time series analysis method. This study was conducted to determine the best model for forecasting gold commodity prices as well as forecasting world gold commodity prices in 2018 using Box-Jenkins approach. The data used in this study wasobtained from Investing.Com from 2015 until 2017. Thisstudy shows that ARIMA (1,1,1) is the best model to predict gold commodity prices based on Mean Absolute Percentage Error (MAPE). MAPE value for ARIMA (1,1,1) is 0.02%, where this value proves that forecasting using ARIMA (1,1,1) is the best forecasting becauseMAPE value is less than 10%.
    Matched MeSH terms: Forecasting
  18. An ensemble model for forecasting infectious diseases in India
    Shashvat K, Basu R, Bhondekar PA, Kaur A
    Trop Biomed, 2019 Dec 01;36(4):822-832.
    PMID: 33597454
    Time series modelling and forecasting plays an important role in various domains. The objective of this paper is to construct a simple average ensemble method to forecast the number of cases for infectious diseases like dengue and typhoid and compare it by applying models for forecasting. In this paper we have also evaluated the correlation between the number of typhoid and dengue cases with the ecological variables. The monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. This data was analysed by three models namely support vector regression, neural network and linear regression. The proposed simple average ensemble model was constructed by ensemble of three applied regression models i.e. SVR, NN and LR. We combine the regression models based upon the error metrics such as Mean Square Error, Root Mean Square Error and Mean Absolute Error. It was found that proposed ensemble method performed better in terms of forecast measures. The finding demonstrates that the proposed model outperforms as compared to already available applied models on the basis of forecast accuracy.
    Matched MeSH terms: Forecasting
  19. Typhoid fever--important issues still remain
    Pang T, Levine MM, Ivanoff B, Wain J, Finlay BB
    Trends Microbiol., 1998 Apr;6(4):131-3.
    PMID: 9587187
    Matched MeSH terms: Forecasting
  20. A Horizon Scan of Emerging Issues for Global Conservation in 2019
    Sutherland WJ, Broad S, Butchart SHM, Clarke SJ, Collins AM, Dicks LV, et al.
    Trends Ecol Evol, 2019 01;34(1):83-94.
    PMID: 30554808 DOI: 10.1016/j.tree.2018.11.001
    We present the results of our tenth annual horizon scan. We identified 15 emerging priority topics that may have major positive or negative effects on the future conservation of global biodiversity, but currently have low awareness within the conservation community. We hope to increase research and policy attention on these areas, improving the capacity of the community to mitigate impacts of potentially negative issues, and maximise the benefits of issues that provide opportunities. Topics include advances in crop breeding, which may affect insects and land use; manipulations of natural water flows and weather systems on the Tibetan Plateau; release of carbon and mercury from melting polar ice and thawing permafrost; new funding schemes and regulations; and land-use changes across Indo-Malaysia.
    Matched MeSH terms: Forecasting*
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links