| 1. |
- Ai, Sizhi, et al.
(författare)
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Causal associations of short and long sleep durations with 12 cardiovascular diseases : linear and nonlinear Mendelian randomization analyses in UK Biobank
- 2021
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Ingår i: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 42:34, s. 3349-3357
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Tidskriftsartikel (refereegranskat)abstract
- Aims Observational studies have suggested strong associations between sleep duration and many cardiovascular diseases (CVDs), but causal inferences have not been confirmed. We aimed to determine the causal associations between genetically predicted sleep duration and 12 CVDs using both linear and nonlinear Mendelian randomization (MR) designs. Methods and results Genetic variants associated with continuous, short (<= 6 h) and long (>= 9 h) sleep durations were used to examine the causal associations with 12 CVDs among 404 044 UK Biobank participants of White British ancestry. Linear MR analyses showed that genetically predicted sleep duration was negatively associated with arterial hypertension, atrial fibrillation, pulmonary embolism, and chronic ischaemic heart disease after correcting for multiple tests (P <0.001). Nonlinear MR analyses demonstrated nonlinearity (L-shaped associations) between genetically predicted sleep duration and four CVDs, including arterial hypertension, chronic ischaemic heart disease, coronary artery disease, and myocardial infarction. Complementary analyses provided confirmative evidence of the adverse effects of genetically predicted short sleep duration on the risks of 5 out of the 12 CVDs, including arterial hypertension, pulmonary embolism, coronary artery disease, myocardial infarction, and chronic ischaemic heart disease (P< 0.001), and suggestive evidence for atrial fibrillation (P < 0.05). However, genetically predicted long sleep duration was not associated with any CVD. Conclusion This study suggests that genetically predicted short sleep duration is a potential causal risk factor of several CVDs, while genetically predicted long steep duration is unlikely to be a causal risk factor for most CVDs. [GRAPHICS] .
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| 2. |
- Beyaztas, Ufuk, et al.
(författare)
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Construction of functional data analysis modeling strategy for global solar radiation prediction : application of cross-station paradigm
- 2019
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 1165-1181
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Tidskriftsartikel (refereegranskat)abstract
- To support initiatives for global emissions targets set by the United Nations Framework Convention on climate change, sustainable extraction of usable power from freely-available global solar radia- tion as a renewable energy resource requires accurate estimation and forecasting models for solar energy. Understanding the Global Solar Radiation (GSR) pattern is highly significant for determin- ing the solar energy in any particular environment. The current study develops a new mathematical model based on the concept of Functional Data Analysis (FDA) to predict daily-scale GSR in the Burk- ina Faso region of West Africa. Eight meteorological stations are adopted to examine the proposed predictive model. The modeling procedure of the regression FDA is performed using two different internal parameter tuning approaches including Generalized Cross-Validation (GCV) and Generalized Bayesian Information Criteria (GBIC). The modeling procedure is established based on a cross-station paradigm wherein the climatological variables of six stations are used to predict GSR at two targeted meteorological stations. The performance of the proposed method is compared with the panel data regression model. Based on various statistical metrics, the applied FDA model attained convincing absolute error measures and best goodness of fit compared with the observed measured GSR. In quantitative evaluation, the predictions of GSR at the uahigouya and Dori stations attained corre- lation coefficients of R 0.84 and 0.90 using the FDA model, respectively. All in all, the FDA model introduced a reliable alternative modeling strategy for global solar radiation prediction over the Burkina Faso region with accurate line fit predictions.
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| 3. |
- Ehteram, Mohammad, et al.
(författare)
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Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks
- 2022
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Ingår i: Journal of Water and Climate. - : IWA Publishing. - 2040-2244 .- 2408-9354. ; 13:10, s. 3620-3643
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater (GW) plays a key role in water supply in basins. As global warming and climate change affect groundwater level (GWL), it is important to predict it for planning and managing water resources. This study investigates the GWL of the Yazd-Ardakan Plain basin in Iran for the base period of 1979-2005 and predicts for periods of 2020-2059 and 2060-2099. Lagged temperature and rainfall are used as inputs to hybrid and standalone artificial neural network (ANN) models. In this study, the rat swarm algorithm (RSA), particle swarm optimisation (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) are used to adjust ANN models. The outcomes of these models are then entered into an inclusive multiple model (IMM) as an ensemble model. In this study, the output of climate models is also inserted into the IMM model to improve the estimation accuracy of temperature, rainfall, and GWL. The monthly average temperature for the base period is 12.9 degrees C, while average temperatures for 2020-2059 under RCP 4.5 and RCP 8.5 scenarios are 14.5 and 15.1 degrees C, and for 2060-2099 they are 16.41 and 18.5 degrees C under the same scenarios, respectively. In future periods, rainfall is low in comparison with the base period. Lagged rainfall and temperature of the base period are inserted into ANN-RSA, ANN-SSA, ANN-PSO, ANN-GA, and ANN models to predict GWL for the base period. Outputs of IMM, ANN, and the five hybrid models (ANN-RSA, ANN-SSA, ANN-PSO, and ANN-GA) indicate that root mean square errors (RMSE) are 2.12, 3.2, 4.58, 6.12, 6.98, and 7.89 m, respectively, in the testing level. It is found that GWL depletion in 2020-2059 under RCP 4.5 and RCP 8.5 scenarios are 0.60-0.88 m and 0.80-1.16 m, and in 2060-2099 under the same scenarios they are 1.49-1.97 m and 1.75-1.98 m, respectively. The results highlight the need to prevent overexploitation of GW in the Ardakan-Yazd Plain to avoid water shortages in the future.
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| 4. |
- Homsi, Rajab, et al.
(författare)
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Precipitation projection using a CMIP5 GCM ensemble model : a regional investigation of Syria
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 90-106
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Tidskriftsartikel (refereegranskat)abstract
- The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.
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| 5. |
- Jing, Wang, et al.
(författare)
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Implementation of evolutionary computing models for reference evapotranspiration modeling : short review, assessment and possible future research directions
- 2019
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 811-823
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Tidskriftsartikel (refereegranskat)abstract
- Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ETo) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ETo is a major concern in hydrology. ETo can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ETo. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ETo. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ETo in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ETo using EC.
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| 6. |
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Machine learning model development for predicting aeration efficiency through Parshall flume
- 2021
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 15:1, s. 889-901
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Tidskriftsartikel (refereegranskat)abstract
- This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting=0.002,R2testing=0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.
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| 7. |
- Malik, Anurag, et al.
(författare)
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Modeling monthly pan evaporation process over the Indian central Himalayas : application of multiple learning artificial intelligence model
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 323-338
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Tidskriftsartikel (refereegranskat)abstract
- The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.
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| 8. |
- Moazenzadeh, Roozbeh, et al.
(författare)
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Soil moisture estimation using novel bio-inspired soft computing approaches
- 2022
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Informa UK Limited. - 1994-2060 .- 1997-003X. ; 16:1, s. 826-840
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Tidskriftsartikel (refereegranskat)abstract
- Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.
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| 9. |
- Salih, Sinan Q., et al.
(författare)
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Thin and sharp edges bodies-fluid interaction simulation using cut-cell immersed boundary method
- 2019
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 860-877
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Tidskriftsartikel (refereegranskat)abstract
- This study aims to develop an adaptive mesh refinement (AMR) algorithm combined with Cut-Cell IBM using two-stage pressure–velocity corrections for thin-object FSI problems. To achieve the objective of this study, the AMR-immersed boundary method (AMR-IBM) algorithm discretizes and solves the equations of motion for the flow that involves rigid thin structures boundary layer at the interface between the structure and the fluid. The body forces are computed in proportion to the fraction of the solid volume in the IBM fluid cells to incorporate fluid and solid motions into the boundary. The corrections of the velocity and pressure is determined by using a novel simplified marker and cell scheme. The new developed AMR-IBM algorithm is validated using a benchmark data of fluid past a cylinder and the results show that there is good agreement under laminar flow. Simulations are conducted for three test cases with the purpose of demonstration the accuracy of the AMR-IBM algorithm. The validation confirms the robustness of the new algorithms in simulating flow characteristics in the boundary layers of thin structures. The algorithm is performed on a staggered grid to simulate the fluid flow around thin object and determine the computational cost.
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| 10. |
- Salih, Sinan Q., et al.
(författare)
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Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation : case study of Nasser Lake in Egypt
- 2019
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 13:1, s. 878-891
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Tidskriftsartikel (refereegranskat)abstract
- Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.
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