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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
A time series based sequence prediction algorithm to detect activities of daily living in smart home

Marufuzzaman M, Reaz MB, Ali MA, Rahman LF

Methods Inf Med, 2015;54(3):262-70.
PMID: 25604028 DOI: 10.3414/ME14-01-0061

OBJECTIVES: The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included.
METHODS: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
RESULTS: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
CONCLUSIONS: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

Matched MeSH terms: Forecasting
Fulltext Partial discharge characteristics of polymer nanocomposite materials in electrical insulation: a review of sample preparation techniques, analysis methods, potential applications, and future trends

Izzati WA, Arief YZ, Adzis Z, Shafanizam M

ScientificWorldJournal, 2014;2014:735070.
PMID: 24558326 DOI: 10.1155/2014/735070

Polymer nanocomposites have recently been attracting attention among researchers in electrical insulating applications from energy storage to power delivery. However, partial discharge has always been a predecessor to major faults and problems in this field. In addition, there is a lot more to explore, as neither the partial discharge characteristic in nanocomposites nor their electrical properties are clearly understood. By adding a small amount of weight percentage (wt%) of nanofillers, the physical, mechanical, and electrical properties of polymers can be greatly enhanced. For instance, nanofillers in nanocomposites such as silica (SiO2), alumina (Al2O3) and titania (TiO2) play a big role in providing a good approach to increasing the dielectric breakdown strength and partial discharge resistance of nanocomposites. Such polymer nanocomposites will be reviewed thoroughly in this paper, with the different experimental and analytical techniques used in previous studies. This paper also provides an academic review about partial discharge in polymer nanocomposites used as electrical insulating material from previous research, covering aspects of preparation, characteristics of the nanocomposite based on experimental works, application in power systems, methods and techniques of experiment and analysis, and future trends.

Matched MeSH terms: Forecasting
Forecasting asthma-related hospital admissions in London using negative binomial models

Soyiri IN, Reidpath DD, Sarran C

Chron Respir Dis, 2013 May;10(2):85-94.
PMID: 23620439 DOI: 10.1177/1479972313482847

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Matched MeSH terms: Forecasting
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
MIMO model of an interacting series process for Robust MPC via System Identification

Wibowo TC, Saad N

ISA Trans, 2010 Jul;49(3):335-47.
PMID: 20304404 DOI: 10.1016/j.isatra.2010.02.005

This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.

Matched MeSH terms: Forecasting
Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Ghazali NA, Ramli NA, Yahaya AS, Yusof NF, Sansuddin N, Al Madhoun WA

Environ Monit Assess, 2010 Jun;165(1-4):475-89.
PMID: 19440846 DOI: 10.1007/s10661-009-0960-3

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

Matched MeSH terms: Forecasting
Comparison between field experiment and PERSIST model simulation: dissipation of fenvalerate in a Malaysian agricultural soil

Ismail BS, Maznah Z

Bull Environ Contam Toxicol, 2005 Jun;74(6):1143-50.
PMID: 16158853
Matched MeSH terms: Forecasting
Solid waste forecasting using modified ANFIS modeling

Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, K N A M

J Air Waste Manag Assoc, 2015 Oct;65(10):1229-38.
PMID: 26223583 DOI: 10.1080/10962247.2015.1075919

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98.

Matched MeSH terms: Forecasting
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
Predicting arsenic solubility in contaminated soils using isotopic dilution techniques

Tye AM, Young SD, Crout NM, Zhang H, Preston S, Bailey EH, et al.

Environ Sci Technol, 2002 Mar 1;36(5):982-8.
PMID: 11924544

An isotopic dilution assay was developed to measure radiolabile As concentration in a diverse range of soils (pH 3.30-7.62; % C = 1.00-6.55). Soils amended with 50 mg of As kg(-1) (as Na2HAsO4 x 7H2O) were incubated for over 800 d in an aerated "microcosm" experiment. After 818 d, radiolabile As ranged from 27 to 57% of total applied As and showed a pH-dependent increase above pH 6. The radiolabile assay was also applied to three sets of soils historically contaminated with sewage sludge or mine-spoil. Results reflected the various geochemical forms in which the arsenic was present. On soils from a sewage disposal facility, radiolabile arsenate ranged from 3 to 60% of total As; mean lability was lower than in the equivalent pH range of the microcosm soils, suggesting occlusion of As into calcium phosphate compounds in the sludge-amended soils. In soils from mining areas in the U.K. and Malaysia, radiolabile As accounted for 0.44-19% of total As. The lowest levels of lability were associated with extremely large As concentrations, up to 17,000 mg kg(-1), from arsenopyrite. Soil pore water was extracted from the microcosm experiment and speciated using "GEOCHEM". The solid<==>solution equilibria of As in the microcosm soils was described by a simple model based on competition between HAsO4(2-) and HPO4(2-) for "labile" adsorption sites.

Matched MeSH terms: Forecasting
Dengue vector control: present status and future prospects

Yap HH, Chong NL, Foo AE, Lee CY

Gaoxiong Yi Xue Ke Xue Za Zhi, 1994 Dec;10 Suppl:S102-8.
PMID: 7844836

Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) have been the most common urban diseases in Southeast Asia since the 1950s. More recently, the diseases have spread to Central and South America and are now considered as worldwide diseases. Both Aedes aegypti and Aedes albopictus are involved in the transmission of DF/DHF in Southeast Asian region. The paper discusses the present status and future prospects of Aedes control with reference to the Malaysian experience. Vector control approaches which include source reduction and environmental management, larviciding with the use of chemicals (synthetic insecticides and insect growth regulators and microbial insecticide), and adulticiding which include personal protection measures (household insecticide products and repellents) for long-term control and space spray (both thermal fogging and ultra low volume sprays) as short-term epidemic measures are discussed. The potential incorporation of IGRs and Bacillus thuringiensis-14 (Bti) as larvicides in addition to insecticides (temephos) is discussed. The advantages of using water-based spray over the oil-based (diesel) spray and the use of spray formulation which provide both larvicidal and adulticidal effects that would consequently have greater impact on the overall vector and disease control in DF/DHF are highlighted.

Matched MeSH terms: Forecasting
Present status of cancer treatment in several Asian countries

Puribhat S

Gan To Kagaku Ryoho, 1992 Jul;19(8 Suppl):1153-9.
PMID: 1514828

Most of Asian Countries are still developing. Hence there are constraints in cancer treatment. There are those countries with fully equipped and fully distributed like western world such as Japan, Korea and Singapore. Other with some comprehensive cancer centers but confine to only big cities with poor coverage of the population resulting in a lot of late cases of cancer patients seen. Such countries are Bangladesh, China, India, Indonesia, Malaysia, Sri-Lanka, Thailand and etc. Still a lot of countries have no facilities to cope with cancer patients such as Brunei, Kampuchea, Laos, Nepal, Vietnam and etc. International collaboration and supports are needed.

Matched MeSH terms: Forecasting
Prospects and programs for fertility reduction, 1990-2015

Mauldin WP, Ross JA

Stud Fam Plann, 1994 Mar-Apr;25(2):77-95.
PMID: 8059448 DOI: 10.2307/2138086

What is the likelihood that each of the 37 developing countries with populations of 15 million or more in 1990 will reach replacement fertility by the year 2015? These countries have a combined population of 3.9 billion, 91 percent of the population of all developing countries. For this article, a composite index was used as the basis for predicting future levels of total fertility. The index was constructed from socioeconomic variables (life expectancy at birth, infant mortality rates, percent adult literacy, ratio of children enrolled in primary or secondary school, percent of the labor force in nonagricultural occupations, gross national product per capita, and percent of the population living in urban areas), total fertility rates for the years 1985-90, total fertility rate decline from 1960-65 to 1985-90, family planning program effort scores in 1989, and the level of contraceptive prevalence in 1990. Eight countries are classified as certain to reach replacement fertility by 2015, and an additional thirteen probably will also. Five countries are classified as possibly reaching replacement fertility, and eleven as unlikely to do so.

Matched MeSH terms: Forecasting
Mortality in Asia

WHO Chron, 1981;35(5):163-7.
PMID: 7324457
Matched MeSH terms: Forecasting
Radarsat observations and forecasting of oil slick trajectory movements

Marghany M

J Environ Sci (China), 2004;16(1):44-8.
PMID: 14971450

RADARSAT data have a potential role for coastal pollution monitoring. This study presents a new approach to detect and forecast oil slick trajectory movements. The oil slick trajectory movements is based on the tidal current effects and Fay's algorithm for oil slick spreading mechanisms. The oil spill trajectory model contains the integration between Doppler frequency shift model and Lagrangian model. Doppler frequency shift model implemented to simulate tidal current pattern from RADARSAT data while the Lagrangian model used to predict oil spill spreading pattern. The classical Fay's algorithm was implemented with the two models to simulate the oil spill trajectory movements. The study shows that the slick lengths are effected by tidal current V component with maximum velocity of 1.4 m/s. This indicates that oil slick trajectory path is moved towards the north direction. The oil slick parcels are accumulated along the coastline after 48 h. The analysis indicated that tidal current V components were the dominant forcing for oil slick spreading.

Matched MeSH terms: Forecasting
Statistical prediction of terrestrial gamma radiation dose rate based on geological features and soil types in Kota Tinggi district, Malaysia

Ramli AT, Rahman AT, Lee MH

Appl Radiat Isot, 2003 Nov-Dec;59(5-6):393-405.
PMID: 14622942

A statistical prediction of terrestrial gamma radiation dose rate has been performed, covering the Kota Tinggi district of Peninsular Malaysia. The prediction has been based on geological features and soil types. The purpose of this study is to provide a methodology to statistically predict the gamma radiation dose rate with minimum surveying in an area. Results of statistical predictions using the hypothesis test were compared with the actual dose rate obtained by measurements.

Matched MeSH terms: Forecasting
Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model

Dikshit A, Pradhan B, Alamri AM

Sci Total Environ, 2021 Feb 10;755(Pt 2):142638.
PMID: 33049536 DOI: 10.1016/j.scitotenv.2020.142638

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.

Matched MeSH terms: Forecasting
Fulltext Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

Ghazvinian H, Mousavi SF, Karami H, Farzin S, Ehteram M, Hossain MS, et al.

PLoS One, 2019;14(5):e0217634.
PMID: 31150467 DOI: 10.1371/journal.pone.0217634

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.

Matched MeSH terms: Forecasting
Incentive spirometry inspiratory capacity changes and predictors after open heart surgery: a 5-day prospective study

Narayanan LT, Hamid SRGS

Med J Malaysia, 2020 05;75(3):226-234.
PMID: 32467537

INTRODUCTION: Incentive spirometry (IS) is commonly used for increasing postoperative IS inspiratory capacity (ISIC) after open heart surgery (OHS). However, little is known about the serial changes in ISIC and their predictive factors.
OBJECTIVE: The aim of this study is to identify the postoperative ISIC changes relative to preoperative ISIC after OHS, and determine their predictors, including patient characteristics factors and IS performance parameters such as inspiration volumes (ISv) and frequencies (ISf).
METHODS: This is a prospective study with blinding procedures involving 95 OHS patients, aged 52.8±11.5 years, whose ISIC was measured preoperatively (PreopISIC) until fifth postoperative day (POD), while ISv and ISf monitored with an electronic device from POD1-POD4. Regression models were used to identify predictors of POD1 ISIC, POD2- POD5 ISIC increments, and the odds of attaining PreopISIC by POD5.
RESULTS: The ISIC reduced to 41% on POD1, increasing thereafter to 57%, 75%, 91%, and 106% from POD2-POD5 respectively. Higher PreopISIC (B=-0.01) significantly predicted lower POD1 ISIC, and, together with hyperlipedemia (B=11.52), which significantly predicted higher POD1 ISIC, explained 13% of variance. ISv at relative percentages of PreopISIC from POD1-POD4 (BPOD1=0.60, BPOD2=0.56, BPOD3=0.49, BPOD4=0.50) significantly predicted ISIC of subsequent PODs with variances at 23%, 24%, 17% and 25% respectively, but no association was elicited for ISf. IS performance findings facilitated proposal of a postoperative IS therapy target guideline. Higher ISv (B=0.05) also increased odds of patients recovering to preoperative ISIC on POD5 while higher PreopISIC (B=- 0.002), pain (B=-0.72) and being of Indian race (B=-1.73) decreased its odds.
CONCLUSION: ISv appears integral to IS therapy efficacy after OHS and the proposed therapy targets need further verification through randomized controlled trials.

Matched MeSH terms: Forecasting

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