| 1. |
- Tao, Hai, et al.
(författare)
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Global solar radiation prediction over North Dakota using air temperature : Development of novel hybrid intelligence model
- 2021
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Ingår i: Energy Reports. - Netherland : Elsevier. - 2352-4847. ; 7, s. 136-157
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Tidskriftsartikel (refereegranskat)abstract
- Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFISDragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction ccuracy.
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| 2. |
- Tao, Hai, et al.
(författare)
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Training and Testing Data Division Influence on Hybrid Machine Learning Model Process : Application of River Flow Forecasting
- 2020
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Ingår i: Complexity. - London : Hindawi Publishing Corporation. - 1076-2787 .- 1099-0526. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 3. |
- Yaseen, Zaher Mundher, et al.
(författare)
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Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 70-89
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- 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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| 5. |
- Bhagat, Suraj Kumar, et al.
(författare)
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Evaluating Physical and Fiscal Water Leakage in Water Distribution System
- 2019
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Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 11:10
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Tidskriftsartikel (refereegranskat)abstract
- With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$ and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549,627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.
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| 6. |
- Fu, Minglei, et al.
(författare)
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Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale : Application in Daily Streamflow Simulation
- 2020
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Ingår i: IEEE Access. - USA : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8:1, s. 32632-32651
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Tidskriftsartikel (refereegranskat)abstract
- Streamow forecasting is essential for hydrological engineering. In accordance with theadvancement of computer aids in this eld, various machine learning (ML) models have been explored tosolve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly exploredversion of an ML model called the long short-term memory (LSTM) was investigated for streamowprediction using historical data for forecasting for a particular period. For a case study located in a tropicalenvironment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. Themodelling was performed according to several perspectives: (i) The feasibility of applying the developedLSTM model to streamow prediction was veried, and the performance of the developed LSTM modelwas compared with the classic backpropagation neural network model; (ii) In the experimental process ofapplying the LSTM model to the prediction of streamow, the inuence of the training set size on theperformance of the developed LSTM model was tested; (iii) The effect of the time interval between thetraining set and the testing set on the performance of the developed LSTM model was tested; (iv) The effectof the time span of the prediction data on the performance of the developed LSTM model was tested. Theexperimental data showthat not only does the developedLSTM model have obvious advantages in processingsteady streamow data in the dry season but it also shows good ability to capture data features in the rapidlyuctuant streamow data in the rainy season.
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| 7. |
- Hadi, Sinan Jasim, et al.
(författare)
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Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation
- 2019
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Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 7, s. 141533-141548
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Tidskriftsartikel (refereegranskat)abstract
- Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.
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| 8. |
- Hadi, Sinan Jasim, et al.
(författare)
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The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 101993-102006
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid models that combine wavelet transformation (WT) as a pre-processing tool with data-driven models (DDMs) as modeling approaches have been widely investigated for forecasting streamflow. The WT approach has been applied to original time series for decomposing processes prior to the application of DDM modeling. This procedure has been applied to eliminate redundant patterns or information that lead to a dramatic increase in the model performance. In this study, three experiments were implemented, including stand-alone data-driven modeling, hind cast decomposing using WT divided and entered into the extreme learning machine (ELM), and the extreme gradient boosting (XGB) model to forecast streamflow data. The WT method was applied in two forms: discrete and continuous (DWT and CWT). In this paper, a new hybrid model is proposed based on an integrative prediction model where XGB is used as an input selection tool for the importance attributes of the prediction matrix that are then supplied to the ELM model as a predictive model. The monthly streamflow, upstream flow, rainfall, temperature, and potential evapotranspiration of a basin named in 1805 and located in the south east of Turkey, are used for development of the model. The modeling results show that applying the WT method improved the performance in the hindcast experiment based on the CWT form with minimum root mean square error (RMSE = 4.910 m 3 /s). On the contrary, WT deteriorated the performance of the forecasting and the stand-alone models exhibited a better performance. WT increased the performance of the hindcast experiment due to the inclusion of future information caused by convolution of the time series. However, the forecast experiment experienced deterioration due to the border effect at the end of the time series. Hence, WT was found not to be a useful pre-processing technique in forecasting the streamflow.
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| 9. |
- Hai, Tao, et al.
(författare)
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Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
- 2020
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Ingår i: IEEE Access. - USA : IEEE. - 2169-3536. ; 8, s. 12026-12042
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Tidskriftsartikel (refereegranskat)abstract
- Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).
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| 10. |
- 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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