Displaying publications 61 - 80 of 252 in total

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  1. Fulltext The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges
    Cheng HS, Tan SP, Wong DMK, Koo WLY, Wong SH, Tan NS
    Int J Mol Sci, 2023 Mar 15;24(6).
    PMID: 36982702 DOI: 10.3390/ijms24065633
    Blood is conventionally thought to be sterile. However, emerging evidence on the blood microbiome has started to challenge this notion. Recent reports have revealed the presence of genetic materials of microbes or pathogens in the blood circulation, leading to the conceptualization of a blood microbiome that is vital for physical wellbeing. Dysbiosis of the blood microbial profile has been implicated in a wide range of health conditions. Our review aims to consolidate recent findings about the blood microbiome in human health and to highlight the existing controversies, prospects, and challenges around this topic. Current evidence does not seem to support the presence of a core healthy blood microbiome. Common microbial taxa have been identified in some diseases, for instance, Legionella and Devosia in kidney impairment, Bacteroides in cirrhosis, Escherichia/Shigella and Staphylococcus in inflammatory diseases, and Janthinobacterium in mood disorders. While the presence of culturable blood microbes remains debatable, their genetic materials in the blood could potentially be exploited to improve precision medicine for cancers, pregnancy-related complications, and asthma by augmenting patient stratification. Key controversies in blood microbiome research are the susceptibility of low-biomass samples to exogenous contamination and undetermined microbial viability from NGS-based microbial profiling, however, ongoing initiatives are attempting to mitigate these issues. We also envisage future blood microbiome research to adopt more robust and standardized approaches, to delve into the origins of these multibiome genetic materials and to focus on host-microbe interactions through the elaboration of causative and mechanistic relationships with the aid of more accurate and powerful analytical tools.
    Matched MeSH terms: Forecasting
  2. Fulltext Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022
    Wan Mohamad Nawi WIA, K Abdul Hamid AA, Lola MS, Zakaria S, Aruchunan E, Gobithaasan RU, et al.
    PLoS One, 2023;18(5):e0285407.
    PMID: 37172040 DOI: 10.1371/journal.pone.0285407
    Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.
    Matched MeSH terms: Forecasting
  3. Fulltext Underrepresented Populations in Parkinson's Genetics Research: Current Landscape and Future Directions
    Schumacher-Schuh AF, Bieger A, Okunoye O, Mok KY, Lim SY, Bardien S, et al.
    Mov Disord, 2022 Aug;37(8):1593-1604.
    PMID: 35867623 DOI: 10.1002/mds.29126
    BACKGROUND: Human genetics research lacks diversity; over 80% of genome-wide association studies have been conducted on individuals of European ancestry. In addition to limiting insights regarding disease mechanisms, disproportionate representation can create disparities preventing equitable implementation of personalized medicine.

    OBJECTIVE: This systematic review provides an overview of research involving Parkinson's disease (PD) genetics in underrepresented populations (URP) and sets a baseline to measure the future impact of current efforts in those populations.

    METHODS: We searched PubMed and EMBASE until October 2021 using search strings for "PD," "genetics," the main "URP," and and the countries in Latin America, Caribbean, Africa, Asia, and Oceania (excluding Australia and New Zealand). Inclusion criteria were original studies, written in English, reporting genetic results on PD from non-European populations. Two levels of independent reviewers identified and extracted information.

    RESULTS: We observed imbalances in PD genetic studies among URPs. Asian participants from Greater China were described in the majority of the articles published (57%), but other populations were less well studied; for example, Blacks were represented in just 4.0% of the publications. Also, although idiopathic PD was more studied than monogenic forms of the disease, most studies analyzed a limited number of genetic variants. We identified just nine studies using a genome-wide approach published up to 2021, including URPs.

    CONCLUSION: This review provides insight into the significant lack of population diversity in PD research highlighting the immediate need for better representation. The Global Parkinson's Genetics Program (GP2) and similar initiatives aim to impact research in URPs, and the early metrics presented here can be used to measure progress in the field of PD genetics in the future. © 2022 International Parkinson and Movement Disorder Society.

    Matched MeSH terms: Forecasting
  4. Fulltext Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences
    Yang S, Li X, Jiang Z, Xiao M
    PLoS One, 2023;18(10):e0290126.
    PMID: 37844110 DOI: 10.1371/journal.pone.0290126
    Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.
    Matched MeSH terms: Forecasting
  5. 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
  6. Fulltext Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models
    Chan Phooi M'ng J, Mehralizadeh M
    PLoS One, 2016;11(6):e0156338.
    PMID: 27248692 DOI: 10.1371/journal.pone.0156338
    The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today's increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong's Hang Seng futures, Japan's NIKKEI 225 futures, Singapore's MSCI futures, South Korea's KOSPI 200 futures, and Taiwan's TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.
    Matched MeSH terms: Forecasting
  7. Fulltext Deep learning in public health: Comparative predictive models for COVID-19 case forecasting
    Tariq MU, Ismail SB
    PLoS One, 2024;19(3):e0294289.
    PMID: 38483948 DOI: 10.1371/journal.pone.0294289
    The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.
    Matched MeSH terms: Forecasting
  8. Fulltext KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling
    Ismail AM, Ab Hamid SH, Abdul Sani A, Mohd Daud NN
    PLoS One, 2024;19(4):e0299585.
    PMID: 38603718 DOI: 10.1371/journal.pone.0299585
    The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results.
    Matched MeSH terms: Forecasting
  9. Fulltext Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts
    Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, et al.
    J ISAKOS, 2024 Apr;9(2):227-233.
    PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015
    Matched MeSH terms: Forecasting
  10. 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*
  11. Information technology in health care--what the future holds
    Bulgiba AM
    Asia Pac J Public Health, 2004;16(1):64-71.
    PMID: 18839870 DOI: 10.1177/101053950401600111
    In 1998, Malaysia opened its first hospital based on the "paperless and filmless" concept. Two are now in operation, with more to follow. Telemedicine is now being used in some hospitals and is slated to be the technology to watch. Future use of technology in health care will centre on the use of centralised patient databases and more effective use of artificial intelligence. Stumbling blocks include the enormous capital costs involved and difficulty in getting sufficient bandwidth to support applications on a national scale. Problems with the use of information technology in developing countries still remain; mainly inadequate skilled resources to operate and maintain the technology, lack of home-grown technology, insufficient experience in the use of information technology in health care and the attitudes of some health staff. The challenge for those involved in this field will not be in building new "paperless and filmless" institutions but in transforming current "paper and film-based" institutions to "paperless and filmless" ones and changing the mindset of health staff. Universities and medical schools must be prepared to respond to this new wave by incorporating elements of medical/health informatics in their curriculum and assisting governments in the planning and implementation of these projects. The experience of the UMMC is highlighted as an example of the difficulty of transforming a paper-based hospital to a "paperless and filmless" hospital.
    Matched MeSH terms: Forecasting*
  12. Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia
    Yong NK, Awang N
    Environ Monit Assess, 2019 Jan 11;191(2):64.
    PMID: 30635772 DOI: 10.1007/s10661-019-7209-6
    This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
    Matched MeSH terms: Forecasting/methods*
  13. Fulltext Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
    Vijayasarveswari V, Andrew AM, Jusoh M, Sabapathy T, Raof RAA, Yasin MNM, et al.
    PLoS One, 2020;15(8):e0229367.
    PMID: 32790672 DOI: 10.1371/journal.pone.0229367
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
    Matched MeSH terms: Forecasting/methods*
  14. Fulltext Modelling monthly influenza cases in Malaysia
    Norrulashikin MA, Yusof F, Hanafiah NHM, Norrulashikin SM
    PLoS One, 2021;16(7):e0254137.
    PMID: 34288925 DOI: 10.1371/journal.pone.0254137
    The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.
    Matched MeSH terms: Forecasting/methods*
  15. Fulltext An overview of health forecasting
    Soyiri IN, Reidpath DD
    Environ Health Prev Med, 2013 Jan;18(1):1-9.
    PMID: 22949173 DOI: 10.1007/s12199-012-0294-6
    Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.
    Matched MeSH terms: Forecasting*
  16. 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
  17. Medical schools in Malaysia--meeting the future
    Jalaludin MA, Arokiasamy JT
    Med J Malaysia, 2002 Dec;57 Suppl E:3-4.
    PMID: 12733183
    Matched MeSH terms: Forecasting
  18. Fulltext The future of the GP
    Wilkinson IE
    Br J Gen Pract, 1992 Feb;42(355):84.
    PMID: 1493024
    Matched MeSH terms: Forecasting
  19. Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
    Mahmoud khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin
    Sains Malaysiana, 2016;45:19-28.
    Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of feed-forward, Elman and Cascade forward neural networks with different algorithms to estimate groundwater levels in the Langat Basin from 2007 to 2013. In order to examine the accuracy of monthly water level forecasts, effectiveness of the steepness coefficient in the sigmoid function of a developed ANN model was evaluated in this research. The performance of the models was evaluated using the mean squared error (MSE) and the correlation coefficient (R). The results indicated that the ANN technique was well suited for forecasting groundwater levels. All models developed had shown acceptable results. Based on the observation, the feed-forward neural network model optimized with the Levenberg-Marquardt algorithms showed the most beneficial results with the minimum MSE value of (0.048) and maximum R value of (0.839), obtained for simulation of groundwater levels. The present research conclusively showed the capability of ANNs to provide excellent estimation accuracy and valuable sensitivity analyses.
    Matched MeSH terms: Forecasting
  20. Fulltext Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
    Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S
    Sci Rep, 2021 Feb 09;11(1):3435.
    PMID: 33564055 DOI: 10.1038/s41598-021-82977-9
    A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
    Matched MeSH terms: Forecasting
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