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The peak period of an energy-generating wave is one of the most important parameters that describe the spectral shape of the oceanic wave, as this indicates the duration for which the waves prevail with respect to their maximum extractable energy. In this paper, a half-hourly peak wave energy period (TP) forecast model is constructed using a suite of statistically significant lagged inputs based on the partial auto-correlation function with an extreme learning machine model developed and its predictive utility is benchmarked against deep learning models, i.e., convolutional neural network (CNN/CovNet) and recurrent neural network (RNN) models and other traditional M5tree, Conditional Maximization based Multiple Linear Regression (MLR-ECM) and MLR models. The objective model (ELM) vs. the comparison models (CNN, RNN, M5tree, MLR-ECM, and MLR) were trained and validated independently on the test dataset obtained from coastal zones of eastern Australia that have a high potential for implementation of wave energy generation systems. The outcomes ascertain that the ELM model can generate significantly accurate predictions of the half-hourly peak wave energy period, providing a good level of accuracy relative to deep learning models in selected coastal study zones. The study establishes the practical usefulness of the ELM model as being a noteworthy methodology for the applications in renewable and sustainable energy resource management systems.
Mumtaz Ali; Ramendra Prasad; Yong Xiang; Adarsh Sankaran; Ravinesh C. Deo; Fuyuan Xiao; Shuyu Zhu. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia. Renewable Energy 2021, 177, 1031 -1044.
AMA StyleMumtaz Ali, Ramendra Prasad, Yong Xiang, Adarsh Sankaran, Ravinesh C. Deo, Fuyuan Xiao, Shuyu Zhu. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia. Renewable Energy. 2021; 177 ():1031-1044.
Chicago/Turabian StyleMumtaz Ali; Ramendra Prasad; Yong Xiang; Adarsh Sankaran; Ravinesh C. Deo; Fuyuan Xiao; Shuyu Zhu. 2021. "Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia." Renewable Energy 177, no. : 1031-1044.
Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments. Traditional forecasting methods find it relatively difficult to obtain a reliable solar energy monitoring system because of the inherent nonlinearities in solar radiation and the related atmospheric input variables to any forecasting system. This paper proposes a new artificial intelligence-based hybrid model by employing the robust version of local mean decomposition (RLMD) and Long Short-term Memory (LSTM) network denoted as RLMD-LSTM. The objective model (i.e., RLMD-LSTM) is built near real-time, half-hourly ground-based solar radiation dataset for the solar rich, metropolitan study sites in Vietnam with all of the forecasting results being benchmarked through classical modelling approaches (i.e., Support Vector Regression SVR, Long Short-term Memory LSTM, Multivariate Adaptive Regression Spline MARS, Persistence) as well as the other alternative hybrid methods (i.e., RLMD-MARS, RLMD-Persistence and RLMD-SVR). Verified by statistical metrics and visual infographics, the present results demonstrate that the proposed model can generate satisfactory predictions, outperforming several counterpart methods. The predictive performance is stable for all study sites that the root-mean-square error remained profoundly lower for RLMD-LSTM (19–20%) compared with RLMD-MARS (20–21%), RLMD-SVR (29–35%), RLMD- Persistence (29–51%), LSTM (25–48%), MARS (21–51%) and SVR (23–85%), Persistence (29–51%). The Legates and McCabe’s Index, yielding a value of approximately 0.7988–0.9256 for RLMD-LSTM compared with 0.765–0.8142, 0.4917–0.5711, 0.6900–0.7482, 0.6914–0.7646, 0.4349–0.7170 respectively, for the RLMD-MARS, RLMD-SVR, RLMD-Persistence, LSTM, MARS, SVR, Persistence models, also confirms the outstanding performance of RLMD-LSTM model. Accordingly, the study ascertains that the newly designed approach can be a potential candidate for real-time energy management, renewable energy integration into a power grid and other decisions to optimise the overall system's scheduling and performance.
Anh Ngoc-Lan Huynh; Ravinesh C. Deo; Mumtaz Ali; Shahab Abdulla; Nawin Raj. Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition. Applied Energy 2021, 298, 117193 .
AMA StyleAnh Ngoc-Lan Huynh, Ravinesh C. Deo, Mumtaz Ali, Shahab Abdulla, Nawin Raj. Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition. Applied Energy. 2021; 298 ():117193.
Chicago/Turabian StyleAnh Ngoc-Lan Huynh; Ravinesh C. Deo; Mumtaz Ali; Shahab Abdulla; Nawin Raj. 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition." Applied Energy 298, no. : 117193.
This study investigated the multifractal characteristics of fine resolution (0.25ox0.25°) daily gridded rainfall fields of India over the period 1901–2013 to examine their spatiotemporal variability. The scaling characterization using Multifractal Detrended Fluctuation Analysis (MFDFA) detected short-term persistency and strong multifractality in the majority of rainfall (over 81%) of the grid points. A detailed exploration on the spatial variability of multifractal properties such as Hurst exponent, spectral width, asymmetry index, Hölder exponent are also performed for six rainfall homogenous regions and 34 meteorological subdivisions in India. The results showed that the highest persistence and complexity is noted in the mountainous terrains of northern and northeastern India. The sub-divisional scale analysis showed that the variability of persistence and complexity is the highest in Kerala and lowest at Vidarbha. Further, the evaluation of multifractal properties of rainfall series of pre- and post-1976/77 Pacific climate shift showed an increase in strength of multifractality in 62% grids after the shift. Changes in the status of persistence with respect to 1976/77 is the highest at Uttaranchal subdivision and changes from positive to negative asymmetry was the highest at northwestern (NW) region. Grid points of Peninsular India exhibited least reduction in complexity, multifractality and persistence in the post-1977 period when compared to pre-1977 period.
Adarsh Sankaran; Sagar Rohidas Chavan; Mumtaz Ali; Archana Devarajan Sindhu; Drisya Sasi Dharan; Muhammad Ismail Khan. Spatiotemporal variability of multifractal properties of fineresolution daily gridded rainfall fields over India. Natural Hazards 2021, 106, 1951 -1979.
AMA StyleAdarsh Sankaran, Sagar Rohidas Chavan, Mumtaz Ali, Archana Devarajan Sindhu, Drisya Sasi Dharan, Muhammad Ismail Khan. Spatiotemporal variability of multifractal properties of fineresolution daily gridded rainfall fields over India. Natural Hazards. 2021; 106 (3):1951-1979.
Chicago/Turabian StyleAdarsh Sankaran; Sagar Rohidas Chavan; Mumtaz Ali; Archana Devarajan Sindhu; Drisya Sasi Dharan; Muhammad Ismail Khan. 2021. "Spatiotemporal variability of multifractal properties of fineresolution daily gridded rainfall fields over India." Natural Hazards 106, no. 3: 1951-1979.
A new multi-step, hybrid artificial intelligence-based model is proposed to forecast future precipitation anomalies using relevant historical climate data coupled with large-scale climate oscillation features derived from the most relevant synoptic-scale climate mode indices. First, NSGA (non-dominated sorting genetic algorithm), as a feature selection strategy, is incorporated to search for statistically relevant inputs from climate data (temperature and humidity), sea-surface temperatures (Niño3, Niño3.4 and Niño4) and synoptic-scale indices (SOI, PDO, IOD, EMI, SAM). Next, the SVD (singular value decomposition) algorithm is applied to decompose all selected inputs, thus capturing the most relevant oscillatory features more clearly; then, the monthly lagged data are incorporated into a random forest model to generate future precipitation anomalies. The proposed model is applied in four districts of Pakistan and benchmarked by means of a standalone kernel ridge regression (KRR) model that is integrated with NSGA-SVD (hybrid NSGA-SVD-KRR) and the NSGA-RF and NSGA-KRR baseline models. Based on its high predictive accuracy and versatility, the new model appears to be a pertinent tool for precipitation anomaly forecasting.
Mumtaz Ali; Ravinesh C. Deo; Yong Xiang; Ya Li; Zaher Mundher Yaseen. Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach. Hydrological Sciences Journal 2020, 65, 2693 -2708.
AMA StyleMumtaz Ali, Ravinesh C. Deo, Yong Xiang, Ya Li, Zaher Mundher Yaseen. Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach. Hydrological Sciences Journal. 2020; 65 (16):2693-2708.
Chicago/Turabian StyleMumtaz Ali; Ravinesh C. Deo; Yong Xiang; Ya Li; Zaher Mundher Yaseen. 2020. "Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach." Hydrological Sciences Journal 65, no. 16: 2693-2708.
The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%–10% data division. In addition, it was found to improve the accuracy in forecasting high flow events. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models.
Hai Tao; Ali Omran Al-Sulttani; Ameen Mohammed Salih Ameen; Zainab Hasan Ali; Nadhir Al-Ansari; Sinan Q. Salih; Reham R. Mostafa. Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting. Complexity 2020, 2020, 1 -22.
AMA StyleHai Tao, Ali Omran Al-Sulttani, Ameen Mohammed Salih Ameen, Zainab Hasan Ali, Nadhir Al-Ansari, Sinan Q. Salih, Reham R. Mostafa. Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting. Complexity. 2020; 2020 ():1-22.
Chicago/Turabian StyleHai Tao; Ali Omran Al-Sulttani; Ameen Mohammed Salih Ameen; Zainab Hasan Ali; Nadhir Al-Ansari; Sinan Q. Salih; Reham R. Mostafa. 2020. "Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting." Complexity 2020, no. : 1-22.
The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.
Adarsh Sankaran; Jaromir Krzyszczak; Piotr Baranowski; Archana Devarajan Sindhu; Nandhineekrishna Kumar; Nityanjali Lija Jayaprakash; Vandana Thankamani; Mumtaz Ali. Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States. Atmosphere 2020, 11, 1116 .
AMA StyleAdarsh Sankaran, Jaromir Krzyszczak, Piotr Baranowski, Archana Devarajan Sindhu, Nandhineekrishna Kumar, Nityanjali Lija Jayaprakash, Vandana Thankamani, Mumtaz Ali. Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States. Atmosphere. 2020; 11 (10):1116.
Chicago/Turabian StyleAdarsh Sankaran; Jaromir Krzyszczak; Piotr Baranowski; Archana Devarajan Sindhu; Nandhineekrishna Kumar; Nityanjali Lija Jayaprakash; Vandana Thankamani; Mumtaz Ali. 2020. "Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States." Atmosphere 11, no. 10: 1116.
This paper examined the multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA. The investigation of multifractality of datasets from stations with differing terrain conditions: Dagget, Bakersfield, Santa Maria, Los Angeles and San Diego using the Multifractal Detrended Fluctuation Analysis showed the existence of a long term persistence and multifractality irrespective of the location. The scaling exponents of SR and ET0 time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from seasonal to multi-annual time scale. The multifractal exponents of pressure and relative air humidity are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the mutifractal cross correlation studies for agro-meteorological time series. The temporal evolution of cross-correlation showed similar pattern for all pair-wise associations involving ET0, except for the RH-ET0 link.
Adarsh Sankaran; Jaromir Krzyszczak; Piotr Baranowski; Archana Devarajan Sindhu; Nandhinee Krishna Pradeep; Nithyanjali Lija Jayaprakash; Vandana Thankamani; Mumtaz Ali. Multifractal Cross Correlation Analysis of Agro-meteorological Datasets (Including Reference Evapotranspiration) of California, United States. 2020, 1 .
AMA StyleAdarsh Sankaran, Jaromir Krzyszczak, Piotr Baranowski, Archana Devarajan Sindhu, Nandhinee Krishna Pradeep, Nithyanjali Lija Jayaprakash, Vandana Thankamani, Mumtaz Ali. Multifractal Cross Correlation Analysis of Agro-meteorological Datasets (Including Reference Evapotranspiration) of California, United States. . 2020; ():1.
Chicago/Turabian StyleAdarsh Sankaran; Jaromir Krzyszczak; Piotr Baranowski; Archana Devarajan Sindhu; Nandhinee Krishna Pradeep; Nithyanjali Lija Jayaprakash; Vandana Thankamani; Mumtaz Ali. 2020. "Multifractal Cross Correlation Analysis of Agro-meteorological Datasets (Including Reference Evapotranspiration) of California, United States." , no. : 1.
Globally, major emphasis is currently being put in utilization and optimization of more sustainable and renewable energy resources, to overcome the future energy demand issues and potential energy crises due to many socioeconomic factors. A near-real-time i.e., half-hourly significant wave height (Hsig) forecast model is designed using a suite of selected model input variables where the multiple linear regression (MLR) model, considering the influence of several variables, is optimized by covariance-weighted least squares (CWLS) estimation algorithm to generate a hybridized MLR-CWLS model with a capability to forecast 30-min ahead Hsig values. First, a diagnostic statistical test based on the correlation coefficient is performed to determine relationships between inputs denoting historical behaviour and the target (Hsig) at one lag of 30-min (t – 1) scale. Subsequently, the data are split into training and testing subsets, following a normalization process, and the MLR-CWLS hybridized model is then trained and validated on the testing dataset adopted from eastern coastal zones of Australia that has a high potential for wave energy generation. Hybridized MLR-CWLS model is benchmarked against competing modelling approaches (multivariate adaptive regression splines-MARS, M5 Model Tree, and MLR) via statistical score metrics. The results show that the hybridized MLR-CWLS model is able to generate reliable forecasts of Hsig relative to the counterpart comparison models. The study ascertains the practical utility of the hybridized MLR-CWLS model for Hsig modelling with significant implications for its potential application in wave and ocean energy generation systems, and some of the other renewable and sustainable energy resource management.
Mumtaz Ali; Ramendra Prasad; Yong Xiang; Ravinesh C. Deo. Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms. Renewable and Sustainable Energy Reviews 2020, 132, 110003 .
AMA StyleMumtaz Ali, Ramendra Prasad, Yong Xiang, Ravinesh C. Deo. Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms. Renewable and Sustainable Energy Reviews. 2020; 132 ():110003.
Chicago/Turabian StyleMumtaz Ali; Ramendra Prasad; Yong Xiang; Ravinesh C. Deo. 2020. "Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms." Renewable and Sustainable Energy Reviews 132, no. : 110003.
This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).
Anh Ngoc-Lan Huynh; Ravinesh C. Deo; Duc-Anh An-Vo; Mumtaz Ali; Nawin Raj; Shahab Abdulla. Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network. Energies 2020, 13, 3517 .
AMA StyleAnh Ngoc-Lan Huynh, Ravinesh C. Deo, Duc-Anh An-Vo, Mumtaz Ali, Nawin Raj, Shahab Abdulla. Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network. Energies. 2020; 13 (14):3517.
Chicago/Turabian StyleAnh Ngoc-Lan Huynh; Ravinesh C. Deo; Duc-Anh An-Vo; Mumtaz Ali; Nawin Raj; Shahab Abdulla. 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network." Energies 13, no. 14: 3517.
High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.
Ahmad Sharafati; Masoud Haghbin; Mohammed Suleman Aldlemy; Mohamed H. Mussa; Ahmed W. Al Zand; Mumtaz Ali; Suraj Kumar Bhagat; Nadhir Al-Ansari; Zaher Mundher Yaseen. Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction. Applied Sciences 2020, 10, 3811 .
AMA StyleAhmad Sharafati, Masoud Haghbin, Mohammed Suleman Aldlemy, Mohamed H. Mussa, Ahmed W. Al Zand, Mumtaz Ali, Suraj Kumar Bhagat, Nadhir Al-Ansari, Zaher Mundher Yaseen. Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction. Applied Sciences. 2020; 10 (11):3811.
Chicago/Turabian StyleAhmad Sharafati; Masoud Haghbin; Mohammed Suleman Aldlemy; Mohamed H. Mussa; Ahmed W. Al Zand; Mumtaz Ali; Suraj Kumar Bhagat; Nadhir Al-Ansari; Zaher Mundher Yaseen. 2020. "Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction." Applied Sciences 10, no. 11: 3811.
Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.
Tran Manh Tuan; Luong Thi Hong Lan; Shuo-Yan Chou; Tran Thi Ngan; Le Hoang Son; Nguyen Long Giang; Mumtaz Ali. M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing. Mathematics 2020, 8, 707 .
AMA StyleTran Manh Tuan, Luong Thi Hong Lan, Shuo-Yan Chou, Tran Thi Ngan, Le Hoang Son, Nguyen Long Giang, Mumtaz Ali. M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing. Mathematics. 2020; 8 (5):707.
Chicago/Turabian StyleTran Manh Tuan; Luong Thi Hong Lan; Shuo-Yan Chou; Tran Thi Ngan; Le Hoang Son; Nguyen Long Giang; Mumtaz Ali. 2020. "M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing." Mathematics 8, no. 5: 707.
In the modern world, the computation of vague data is a challenging job. Different theories are presented to deal with such situations. Amongst them, fuzzy set theory and its extensions produced remarkable results. Samrandache extended the theory to a new horizon with the neutrosophic set (NS), which was further extended to interval neutrosophic set (INS). Neutrosophic cubic set (NCS) is the generalized version of NS and INS. This characteristic makes it an exceptional choice to deal with vague and imprecise data. Aggregation operators are key features of decision-making theory. In recent times several aggregation operators were defined in NCS. The intent of this paper is to generalize these aggregation operators by presenting neutrosophic cubic generalized unified aggregation (NCGUA) and neutrosophic cubic quasi-generalized unified aggregation (NCQGUA) operators. The accuracy and precision are a vital tool to minimize the potential threat in decision making. Generally, in decision making methods, alternatives and criteria are considered to evaluate the better outcome. However, sometimes the decision making environment has more components to express the problem completely. These components are named as the state of nature corresponding to each criterion. This complex frame of work is dealt with by presenting the multi-expert decision-making method (MEDMM).
Majid Khan; Muhammad Gulistan; Mumtaz Ali; Wathek Chammam. The Generalized Neutrosophic Cubic Aggregation Operators and Their Application to Multi-Expert Decision-Making Method. Symmetry 2020, 12, 496 .
AMA StyleMajid Khan, Muhammad Gulistan, Mumtaz Ali, Wathek Chammam. The Generalized Neutrosophic Cubic Aggregation Operators and Their Application to Multi-Expert Decision-Making Method. Symmetry. 2020; 12 (4):496.
Chicago/Turabian StyleMajid Khan; Muhammad Gulistan; Mumtaz Ali; Wathek Chammam. 2020. "The Generalized Neutrosophic Cubic Aggregation Operators and Their Application to Multi-Expert Decision-Making Method." Symmetry 12, no. 4: 496.
Dam and powerhouse operation sustainability is a major concern from the hydraulic engineering perspective. Powerhouse operation is one of the main sources of vibrations in the dam structure and hydropower plant; thus, the evaluation of turbine performance at different water pressures is important for determining the sustainability of the dam body. Draft tube turbines run under high pressure and suffer from connection problems, such as vibrations and pressure fluctuation. Reducing the pressure fluctuation and minimizing the principal stress caused by undesired components of water in the draft tube turbine are ongoing problems that must be resolved. Here, we conducted a comprehensive review of studies performed on dams, powerhouses, and turbine vibration, focusing on the vibration of two turbine units: Kaplan and Francis turbine units. The survey covered several aspects of dam types (e.g., rock and concrete dams), powerhouse analysis, turbine vibrations, and the relationship between dam and hydropower plant sustainability and operation. The current review covers the related research on the fluid mechanism in turbine units of hydropower plants, providing a perspective on better control of vibrations. Thus, the risks and failures can be better managed and reduced, which in turn will reduce hydropower plant operation costs and simultaneously increase the economical sustainability. Several research gaps were found, and the literature was assessed to provide more insightful details on the studies surveyed. Numerous future research directions are recommended.
Zaher Mundher Yaseen; Ameen Mohammed Salih Ameen; Mohammed Suleman Aldlemy; Ameen Mohammed Salih; Haitham Abdulmohsin Afan; Senlin Zhu; Ahmed Mohammed Sami Al-Janabi; Nadhir Al-Ansari; Tiyasha Tiyasha; Hai Tao. State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability 2020, 12, 1676 .
AMA StyleZaher Mundher Yaseen, Ameen Mohammed Salih Ameen, Mohammed Suleman Aldlemy, Ameen Mohammed Salih, Haitham Abdulmohsin Afan, Senlin Zhu, Ahmed Mohammed Sami Al-Janabi, Nadhir Al-Ansari, Tiyasha Tiyasha, Hai Tao. State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability. 2020; 12 (4):1676.
Chicago/Turabian StyleZaher Mundher Yaseen; Ameen Mohammed Salih Ameen; Mohammed Suleman Aldlemy; Ameen Mohammed Salih; Haitham Abdulmohsin Afan; Senlin Zhu; Ahmed Mohammed Sami Al-Janabi; Nadhir Al-Ansari; Tiyasha Tiyasha; Hai Tao. 2020. "State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations." Sustainability 12, no. 4: 1676.
Persistent risks of extreme weather events including droughts and floods due to climate change require precise and timely rainfall forecasting. Yet, the naturally occurring non-stationarity entrenched within the rainfall time series lowers the model performances and is an ongoing research endeavour for practicing hydrologists and drought-risk evaluators. In this paper, an attempt is made to resolve the non-stationarity challenges faced by rainfall forecasting models via a complete ensemble empirical mode decomposition (CEEMD) combined with Random Forest (RF) and Kernel Ridge Regression (KRR) algorithms in designing a hybrid CEEMD-RF-KRR model in forecasting rainfall at Gilgit, Muzaffarabad, and Parachinar in Pakistan at monthly time scale. The rainfall time-series data are simultaneously factorized into respective intrinsic mode functions (IMFs) and a residual element using CEEMD. Once the significant lags of each IMF and the residual are identified, both are forecasted using the RF algorithm. Finally, the KRR model is adopted where the forecasted IMFs and the residual components are combined to generate the final forecasted rainfall. The CEEMD-RF-KRR model shows the best performances at all three sites, in comparison to the comparative models, with maximum values of correlation coefficient (0.97–0.99), Willmott’s index (0.94–0.97), Nash-Sutcliffe coefficient (0.94–0.97) and Legates-McCabe’s index (0.74–0.81). Furthermore, the CEEMD-RF-KRR model generated the most accurate results for Gilgit station considering the Legate-McCabe’s index as base assessment criteria in addition to obtaining the lowest magnitudes of RMSE = 2.52 mm and MAE = 1.98 mm. The proposed hybrid CEEMD-RF-KRR model attained better rainfall forecasting accuracy which is imperative for agriculture, water resource management, and early drought/flooding warning systems.
Mumtaz Ali; Ramendra Prasad; Yong Xiang; Zaher Mundher Yaseen. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. Journal of Hydrology 2020, 584, 124647 .
AMA StyleMumtaz Ali, Ramendra Prasad, Yong Xiang, Zaher Mundher Yaseen. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. Journal of Hydrology. 2020; 584 ():124647.
Chicago/Turabian StyleMumtaz Ali; Ramendra Prasad; Yong Xiang; Zaher Mundher Yaseen. 2020. "Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts." Journal of Hydrology 584, no. : 124647.
Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e., data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of SSL process.
Sinan Q. Salih; Ahmad Sharafati; Khabat Khosravi; Hossam Faris; Ozgur Kisi; Hai Tao; Mumtaz Ali; Zaher Mundher Yaseen. River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrological Sciences Journal 2020, 65, 624 -637.
AMA StyleSinan Q. Salih, Ahmad Sharafati, Khabat Khosravi, Hossam Faris, Ozgur Kisi, Hai Tao, Mumtaz Ali, Zaher Mundher Yaseen. River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrological Sciences Journal. 2020; 65 (4):624-637.
Chicago/Turabian StyleSinan Q. Salih; Ahmad Sharafati; Khabat Khosravi; Hossam Faris; Ozgur Kisi; Hai Tao; Mumtaz Ali; Zaher Mundher Yaseen. 2020. "River suspended sediment load prediction based on river discharge information: application of newly developed data mining models." Hydrological Sciences Journal 65, no. 4: 624-637.
Resource allocation via project scheduling plays an important role to balance project time and cost. In this paper, we are designing a framework to handle scheduling problems using neutrosophic activity duration times. We consider important aspects of time-cost tradeoffs while taking into account the real and uncertain situation surrounded by the projects. In real situations, there are many considerable aspects which should be considered by managers, for instance, the tradeoffs between the project completion time and cost and the uncertain conditions of the environment. Since the concept of the deterministic project scheduling and time-cost tradeoffs conflict with the real situation, where in many cases, some data on the activities durations of the project changes during the implementation process. Fuzzy scheduling and time-cost tradeoffs models assume only truth-membership functions dealing with uncertainties surrounded by the projects and their activities duration which are unable to handle indeterminacy and inconsistency. Therefore, neutrosophic theory is a better framework that takes into account the dynamic features of all parameters. Trapezoidal neutrosophic numbers are used to estimate the activities durations in this paper. The crisp model for activities time obtained by applying score and accuracy functions. The goal here is to minimize the cost of projects under uncertain environmental conditions.
Mohamed Abdel-Basset; Mumtaz Ali; Asmaa Atef. Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set. Computers & Industrial Engineering 2020, 141, 106286 .
AMA StyleMohamed Abdel-Basset, Mumtaz Ali, Asmaa Atef. Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set. Computers & Industrial Engineering. 2020; 141 ():106286.
Chicago/Turabian StyleMohamed Abdel-Basset; Mumtaz Ali; Asmaa Atef. 2020. "Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set." Computers & Industrial Engineering 141, no. : 106286.
To meet the future energy demand and avert any looming crises, efforts are being carried out to utilize sustainable and renewable energy resources. In this paper, the naturally occurring non-linearity and non-stationarity deficiencies within the climatological predictors to forecast solar radiation (Rdn) are resolved via a multivariate empirical mode decomposition method (MEMD). First, a set of antecedent weekly lags at timescale (t-1) of input datasets were collated and then were divided into training and testing subsets. The MEMD method is restricted to dissolve the training and testing climatic data independently into intrinsic modes functions (IMFs). As the numbers of total IMFs were very large, the singular value decomposition (SVD) algorithm is accustomed for dimensionality reduction simultaneously capturing the most relevant oscillatory features embedded within the IMFs. Finally, the random forest (RF) model is applied to forecast Rdn at selected solar-rich regions in Australia. The resulting hybrid MEMD-SVD-RF model was established as a consequence of the aforementioned modelling strategy. The results are benchmarked with other comparative models. The hybrid MEMD-SVD-RF model generates better and reliable forecasts having significant implications for renewable and sustainable energy applications and resources management.
Ramendra Prasad; Mumtaz Ali; Yong Xiang; Huma Khan. A double decomposition-based modelling approach to forecast weekly solar radiation. Renewable Energy 2020, 152, 9 -22.
AMA StyleRamendra Prasad, Mumtaz Ali, Yong Xiang, Huma Khan. A double decomposition-based modelling approach to forecast weekly solar radiation. Renewable Energy. 2020; 152 ():9-22.
Chicago/Turabian StyleRamendra Prasad; Mumtaz Ali; Yong Xiang; Huma Khan. 2020. "A double decomposition-based modelling approach to forecast weekly solar radiation." Renewable Energy 152, no. : 9-22.
Nguyen Xuan Thao; Mumtaz Ali; Le Thi Nhung; Hemant Kumar Gianey; Florentin Smarandache. A new multi-criteria decision making algorithm for medical diagnosis and classification problems using divergence measure of picture fuzzy sets. Journal of Intelligent & Fuzzy Systems 2019, 37, 7785 -7796.
AMA StyleNguyen Xuan Thao, Mumtaz Ali, Le Thi Nhung, Hemant Kumar Gianey, Florentin Smarandache. A new multi-criteria decision making algorithm for medical diagnosis and classification problems using divergence measure of picture fuzzy sets. Journal of Intelligent & Fuzzy Systems. 2019; 37 (6):7785-7796.
Chicago/Turabian StyleNguyen Xuan Thao; Mumtaz Ali; Le Thi Nhung; Hemant Kumar Gianey; Florentin Smarandache. 2019. "A new multi-criteria decision making algorithm for medical diagnosis and classification problems using divergence measure of picture fuzzy sets." Journal of Intelligent & Fuzzy Systems 37, no. 6: 7785-7796.
Intuitionistic fuzzy sets are useful for modeling uncertain data of realistic problems. In this paper, we generalize and expand the utility of complex intuitionistic fuzzy sets using the space of quaternion numbers. The proposed representation can capture composite features and convey multi-dimensional fuzzy information via the functions of real membership, imaginary membership, real non-membership, and imaginary non-membership. We analyze the order relations and logic operations of the complex intuitionistic fuzzy set theory and introduce new operations based on quaternion numbers. We also present two quaternion distance measures in algebraic and polar forms and analyze their properties. We apply the quaternion representations and measures to decision-making models. The proposed model is experimentally validated in medical diagnosis, which is an emerging application for tackling patient’s symptoms and attributes of diseases.
Roan Thi Ngan; Le Hoang Son; Mumtaz Ali; Dan E. Tamir; Naphtali D. Rishe; Abraham Kandel. Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making. Applied Soft Computing 2019, 87, 105961 .
AMA StyleRoan Thi Ngan, Le Hoang Son, Mumtaz Ali, Dan E. Tamir, Naphtali D. Rishe, Abraham Kandel. Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making. Applied Soft Computing. 2019; 87 ():105961.
Chicago/Turabian StyleRoan Thi Ngan; Le Hoang Son; Mumtaz Ali; Dan E. Tamir; Naphtali D. Rishe; Abraham Kandel. 2019. "Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making." Applied Soft Computing 87, no. : 105961.
Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation – the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) – were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R2 = .92), and with all variables as inputs at Station II (R2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.
Zaher Mundher Yaseen; Anas Mahmood Al-Juboori; Ufuk Beyaztas; Nadhir Al-Ansari; Kwok-Wing Chau; Chongchong Qi; Mumtaz Ali; Sinan Q. Salih; Shamsuddin Shahid. Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models. Engineering Applications of Computational Fluid Mechanics 2019, 14, 70 -89.
AMA StyleZaher Mundher Yaseen, Anas Mahmood Al-Juboori, Ufuk Beyaztas, Nadhir Al-Ansari, Kwok-Wing Chau, Chongchong Qi, Mumtaz Ali, Sinan Q. Salih, Shamsuddin Shahid. Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models. Engineering Applications of Computational Fluid Mechanics. 2019; 14 (1):70-89.
Chicago/Turabian StyleZaher Mundher Yaseen; Anas Mahmood Al-Juboori; Ufuk Beyaztas; Nadhir Al-Ansari; Kwok-Wing Chau; Chongchong Qi; Mumtaz Ali; Sinan Q. Salih; Shamsuddin Shahid. 2019. "Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models." Engineering Applications of Computational Fluid Mechanics 14, no. 1: 70-89.