| 1. |
- Al-Janabi, Ahmed Mohammed Sami, et al.
(författare)
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Experimental and Numerical Analysis for Earth-Fill Dam Seepage
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:6, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Earth-fill dams are the most common types of dam and the most economical choice. However, they are more vulnerable to internal erosion and piping due to seepage problems that are the main causes of dam failure. In this study, the seepage through earth-fill dams was investigated using physical, mathematical, and numerical models. Results from the three methods revealed that both mathematical calculations using L. Casagrande solutions and the SEEP /Wnumerical model have a plotted seepage line compatible with the observed seepage line in the physical model. However,when the seepage flow intersected the downstream slope and when piping took place, the use of SEEP /Wto calculate the flow rate became useless as it was unable to calculate the volume of water flow in pipes. This was revealed by the big dierence in results between physical and numerical models in the first physical model, while the results were compatible in the second physical model when the seepage line stayed within the body of the dam and low compacted soil was adopted. Seepage analysis for seven dierent configurations of an earth-fill dam was conducted using the SEEP /W model at normal and maximum water levels to find the most appropriate configuration among them. The seven dam configurations consisted of four homogenous dams and three zoned dams. Seepage analysis revealed that if sucient quantity of silty sand soil is available around the proposed dam location, a homogenous earth-fill dam with a medium drain length of 0.5 m thickness is the best design configuration. Otherwise, a zoned earth-fill dam with a central core and 1:0.5 Horizontal to Vertical ratio (H:V) is preferred.
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| 2. |
- Al-Janabi, Ahmed Mohammed Sami, et al.
(författare)
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Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.
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| 3. |
- Armanuos, Asaad M., et al.
(författare)
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Assessing the Effectiveness of Using Recharge Wells for Controlling the Saltwater Intrusion in Unconfined Coastal Aquifers with Sloping Beds : Numerical Study
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:7
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater systems are considered major freshwater sources for many coastal aquifers worldwide. Seawater intrusion (SWI) inland into freshwater coastal aquifers is a common environmental problem that causes deterioration of the groundwater quality. This research investigates the effectiveness of using an injection through a well to mitigate the SWI in sloping beds of unconfined coastal aquifers. The interface was simulated using SEAWAT code. The repulsion ratios due to the length of the SWI wedge (RL) and the area of the saltwater wedge (RA) were computed. A sensitivity analysis was conducted to recognize the change in the confining layer bed slope (horizontal, positive, and negative) and hydraulic parameters of the value of the SWI repulsion ratio. Injection at the toe itself achieved higher repulsion ratios. RL and RA declined if the injection point was located remotely and higher than the toe of the seawater wedge. Installation at the toe achieved a higher RL in positive sloping followed by horizontal and negative slopes. Moreover, the highest value of RA could be reached by injecting at the toe itself with a horizontal bed aquifer, followed by negative and positive slopes. The recharge well is confirmed as one of the most effective applications for the mitigation of SWI in sloping bed aquifers. The Akrotiri case study shows that the proposed recharging water method has a significant impact on controlling SWI and declines in both SWI wedge length and area.
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| 4. |
- Armanuos, Asaad M., et al.
(författare)
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Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation
- 2020
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Ingår i: Atmosphere. - Switzerland : MDPI. - 2073-4433 .- 2073-4433. ; 11:4, s. 1-35
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Tidskriftsartikel (refereegranskat)abstract
- The results of metrological, hydrological, and environmental data analyses are mainlydependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with rPearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.
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| 5. |
- Armanuos, Asaad M., et al.
(författare)
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Underground Barrier Wall Evaluation for Controlling Saltwater Intrusion in Sloping Unconfined Coastal Aquifers
- 2020
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Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 12:9
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Tidskriftsartikel (refereegranskat)abstract
- Barrier walls are considered one of the most effective methods for facilitating the retreat of saltwater intrusion (SWI). This research plans to examine the effect of using barrier walls for controlling of SWI in sloped unconfined aquifers. The sloping unconfined aquifer is considered with three different bed slopes. The SEAWAT model is implemented to simulate the SWI. For model validation, the numerical results of the seawater wedge at steady state were compared with the analytical solution. Increasing the ratio of flow barrier depth (db/d) forced the saltwater interface to move seaward and increased the repulsion ratio (R). With a positive sloping bed, further embedding the barrier wall from 0.2 to 0.7 caused R to increase from 0.3% to 59%, while it increased from 1.8% to 41.7% and from 3.4% to 46.9% in the case of negative and horizontal slopes, respectively. Embedding the barrier wall to a db/d value of more than 0.4 achieved a greater R value in the three bed-sloping cases. Installing the barrier wall near the saltwater side with greater depth contributed to the retreat of the SWI. With a negative bed slope, moving the barrier wall from Xb/Lo = 1.0 toward the saltwater side (Xb/Lo = 0.2) increased R from 7.21% to 68.75%, whereas R increased from 5.3% to 67% for the horizontal sloping bed and from 5.1% to 64% for the positive sloping bed. The numerical results for the Akrotiri coastal aquifer confirm that the embedment of the barrier wall significantly affects the controlling of SWI by increasing the repulsion ratio (R) and decreasing the SWI length ratio (L/La). Cost-benefit analysis is recommended to determine the optimal design of barrier walls for increasing the cost-effectiveness of the application of barrier walls as a countermeasure for controlling and preventing SWI in sloped unconfined aquifers.
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| 6. |
- Bokde, Neeraj, et al.
(författare)
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The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models : Application of Short-Term Wind Speed and Power Modeling
- 2020
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Ingår i: Energies. - Switzerland : MDPI. - 1996-1073. ; 13:7, s. 1-23
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Tidskriftsartikel (refereegranskat)abstract
- In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.
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| 7. |
- Deo, Ravinesh C., et al.
(författare)
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Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction : An Investigation on Engineering Mathematics Courses
- 2020
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8, s. 136697-136724
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Tidskriftsartikel (refereegranskat)abstract
- A computationally efficient artificial intelligence (AI) model called Extreme Learning Machines (ELM) is adopted to analyze patterns embedded in continuous assessment to model the weighted score (WS) and the examination (EX) score in engineering mathematics courses at an Australian regional university. The student performance data taken over a six-year period in multiple courses ranging from the mid- to the advanced level and a diverse course offering mode (i.e., on-campus, ONC, and online, ONL) are modelled by ELM and further benchmarked against competing models: random forest (RF) and Volterra. With the assessments and examination marks as key predictors of WS (leading to a grade in the mid-level course), ELM (with respect to RF and Volterra) outperformed its counterpart models both for the ONC and the ONL offer. This generated relative prediction error in the testing phase, of only 0.74%, compared to about 3.12% and 1.06%, respectively, while for the ONL offer, the prediction errors were only 0.51% compared to about 3.05% and 0.70%. In modelling the student performance in advanced engineering mathematics course, ELM registered slightly larger errors: 0.77% (vs. 22.23% and 1.87%) for ONC and 0.54% (vs. 4.08% and 1.31%) for the ONL offer. This study advocates a pioneer implementation of a robust AI methodology to uncover relationships among student learning variables, developing teaching and learning intervention and course health checks to address issues related to graduate outcomes, and student learning attributes in the higher education sector.
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| 8. |
- 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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| 9. |
- 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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| 10. |
- 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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