This page has only limited features, please log in for full access.
University Educator/Researcher
01 May 2021 - 30 August 2021
One of the most significant parameters in concrete design is compressive strength. Time and money could be saved if the compressive strength of concrete is accurately measured. In this study, two machine learning models, namely, boosted decision tree regression (BDTR) and support vector machine (SVM), were developed to predict concrete compressive strength (CCS) using a complete dataset through the previous scientific studies. Eight concrete mixture parameters were used as the input dataset. Four statistical indices, namely the coefficient of determination (R2) and root mean square error (RMSE), mean absolute error (MAE), and RMSE-Standard Deviation Ratio (RSR), were used to illustrate the efficiency of the proposed models. The results show that the BDTR model outperformed SVM model with the overall result of R2=0.86 and RMSE=6.19 and MAE=4.91 and RSR=0.37, respectively. The results of this study suggest that the compressive strength of high-performance concrete (HPC) can be accurately calculated using the proposed BDTR model.
Sarmad Dashti Latif. Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete. Environmental Science and Pollution Research 2021, 1 .
AMA StyleSarmad Dashti Latif. Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete. Environmental Science and Pollution Research. 2021; ():1.
Chicago/Turabian StyleSarmad Dashti Latif. 2021. "Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete." Environmental Science and Pollution Research , no. : 1.
Phosphate (PO4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpoint-source pollution. The value of the PO4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems.
Sarmad Dashti Latif; Ahmed H. Birima; Ali Najah Ahmed; Dahan Mohammed Hatem; Nadhir Al-Ansari; Chow Ming Fai; Ahmed El-Shafie. Development of prediction model for phosphate in reservoir water system based machine learning algorithms. Ain Shams Engineering Journal 2021, 1 .
AMA StyleSarmad Dashti Latif, Ahmed H. Birima, Ali Najah Ahmed, Dahan Mohammed Hatem, Nadhir Al-Ansari, Chow Ming Fai, Ahmed El-Shafie. Development of prediction model for phosphate in reservoir water system based machine learning algorithms. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleSarmad Dashti Latif; Ahmed H. Birima; Ali Najah Ahmed; Dahan Mohammed Hatem; Nadhir Al-Ansari; Chow Ming Fai; Ahmed El-Shafie. 2021. "Development of prediction model for phosphate in reservoir water system based machine learning algorithms." Ain Shams Engineering Journal , no. : 1.
Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron neural network (MLPNN), support vector machine (SVM), multivariate adaptive regression splines (MARS), genetic programming (GP), M5 algorithm, and group method of data handling (GMDH) were used to predict the piezometric head in the core and the seepage discharge through the body of earth dam. For this purpose, the data recorded by the absolute instrument during the last 94 months of Shahid Kazemi Bukan Dam were used. The results showed that all of the applied models had a permissible level of accuracy in the prediction of the piezometric heads. The average error indices for the models in the training phase were R2= 0.957 and RMSE= 0.806 and in the testing phase were equal to R2= 0.949 and RMSE= 0.932, respectively. The performances of all models except the M5 and MARS in predicting seepage discharge are nearly identical; however, the best is the MARS, and the weakest is the M5 algorithm.
Abbas Parsaie; Amir Hamzeh Haghiabi; Sarmad Dashti Latif; Ravi Prakash Tripathi. Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models. Environmental Science and Pollution Research 2021, 1 -15.
AMA StyleAbbas Parsaie, Amir Hamzeh Haghiabi, Sarmad Dashti Latif, Ravi Prakash Tripathi. Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models. Environmental Science and Pollution Research. 2021; ():1-15.
Chicago/Turabian StyleAbbas Parsaie; Amir Hamzeh Haghiabi; Sarmad Dashti Latif; Ravi Prakash Tripathi. 2021. "Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models." Environmental Science and Pollution Research , no. : 1-15.
Sustainable management of water supplies faces a comprehensive challenge due to global climate change. Improving forecasts of streamflow based on erratic precipitation is a significant activity nowadays. In recent years, the techniques of data-driven have been widely used in the hydrological parameter’s prediction especially streamflow. In the current research, a deep learning model namely Long Short-Term Memory (LSTM), and two conventional machine learning models namely, Random Forest (RF), and Tree Boost (TB) were used to predict the streamflow of the Kowmung river at Cedar Ford in Australia. Different scenarios proposed to determine the optimal combination of input predictor variables, and the input predictor variables were selected based on the auto-correlation function (ACF). Model output was evaluated using indices of the root mean square error (RMSE), and the Nash and Sutcliffe coefficient (NSE). The findings showed that the LSTM model outperformed RF and TB in predicting the streamflow with RMSE and NSE equal to 102.411, and 0.911 respectively. for the LSTM model. The proposed model could adopt by hydrologists to solve the problems associated with forecasting daily streamflow with high precision. This study may not be generalized because of the geographical condition and the nature of the data for each location.
Sarmad Dashti Latif; Ali Najah Ahmed. Application of Deep Learning Method for Daily Streamflow Time-Series Prediction: A Case Study of the Kowmung River at Cedar Ford, Australia. International Journal of Sustainable Development and Planning 2021, 16, 497 -501.
AMA StyleSarmad Dashti Latif, Ali Najah Ahmed. Application of Deep Learning Method for Daily Streamflow Time-Series Prediction: A Case Study of the Kowmung River at Cedar Ford, Australia. International Journal of Sustainable Development and Planning. 2021; 16 (3):497-501.
Chicago/Turabian StyleSarmad Dashti Latif; Ali Najah Ahmed. 2021. "Application of Deep Learning Method for Daily Streamflow Time-Series Prediction: A Case Study of the Kowmung River at Cedar Ford, Australia." International Journal of Sustainable Development and Planning 16, no. 3: 497-501.
Forecasting of reservoir inflow is one of the most vital concerns when it comes to managing water resources at reservoirs to mitigate natural hazards such as flooding. Machine learning (ML) models have become widely prevalent in capturing the complexity of reservoir inflow time-series data. However, the model structure's selection required several trails-and-error processes to identify the optimal architecture to capture the necessary information of various patterns of input–output mapping. In this study, the effectiveness of a deep learning (DL) approach in capturing various input–output patterns is examined and applied to reservoir inflow forecasting. The proposed DL approach has a distinct benefit over classical ML models as all the hidden layers are stacked afterward to train on a diverging set of topologies derived from the previous layer's output. Given the nonlinearity of day-to-day data about reservoir inflow, a deep learning algorithm centered on the long short-term memory (LSTM) and two standard machine learning algorithms, namely support vector machine (SVM) and artificial neural network (ANN), were deployed in this study for forecasting reservoir inflow on a daily basis. The gathered data pertained to historical daily inflow from 01/01/2018 to 31/12/2019. The area of study was Durian Tunggal Reservoir, Melaka, Peninsular Malaysia. The choice of the input set was made on the basis of the autocorrelation function. The formulated model was assessed on the basis of statistical indices, such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2). The outcomes indicate that the LSTM model performed much better than SVM and ANN. Based on the comparison, LSTM outperformed other models with MAE = 0.088, RMSE = 0.27, and R2 = 0.91. This research demonstrates that the deep learning technique is an appropriate method for estimating the daily inflow of the Durian Tunggal Reservoir, unlike the standard machine learning models.
Sarmad Dashti Latif; Ali Najah Ahmed; Edlic Sathiamurthy; Yuk Feng Huang; Ahmed El-Shafie. Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia. Natural Hazards 2021, 1 -19.
AMA StyleSarmad Dashti Latif, Ali Najah Ahmed, Edlic Sathiamurthy, Yuk Feng Huang, Ahmed El-Shafie. Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia. Natural Hazards. 2021; ():1-19.
Chicago/Turabian StyleSarmad Dashti Latif; Ali Najah Ahmed; Edlic Sathiamurthy; Yuk Feng Huang; Ahmed El-Shafie. 2021. "Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia." Natural Hazards , no. : 1-19.
In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.
Sarmad Latif; Suzlyana Marhain; Shabbir Hossain; Ali Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. Sustainability 2021, 13, 5900 .
AMA StyleSarmad Latif, Suzlyana Marhain, Shabbir Hossain, Ali Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia. Sustainability. 2021; 13 (11):5900.
Chicago/Turabian StyleSarmad Latif; Suzlyana Marhain; Shabbir Hossain; Ali Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2021. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia." Sustainability 13, no. 11: 5900.
One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. The production of concrete compressive strength is greatly influenced by severe weather conditions and increases in humidity rates. In this research, a model has been developed to predict concrete compressive strength utilizing a detailed dataset obtained from previously published studies based on a deep learning method, namely, long short-term memory (LSTM), and a conventional machine learning (ML) algorithm, namely, support vector machine (SVM). The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. To demonstrate the efficiency of the proposed models, three statistical indices, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used. Findings shows that LSTM outperformed SVM with R2=0.98, R2= 0.78, MAE=1.861, MAE=6.152, and RMSE=2.36, RMSE=7.93, respectively. The results of this study suggest that high-performance concrete (HPC) compressive strength can be reliably measured using the proposed LSTM model.
Sarmad Dashti Latif. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment. Environmental Science and Pollution Research 2021, 1 -9.
AMA StyleSarmad Dashti Latif. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment. Environmental Science and Pollution Research. 2021; ():1-9.
Chicago/Turabian StyleSarmad Dashti Latif. 2021. "Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment." Environmental Science and Pollution Research , no. : 1-9.
Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.
A. Najah; F. Y. Teo; M. F. Chow; Y. F. Huang; S. D. Latif; S. Abdullah; M. Ismail; A. El-Shafie. Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia. International Journal of Environmental Science and Technology 2021, 18, 1009 -1018.
AMA StyleA. Najah, F. Y. Teo, M. F. Chow, Y. F. Huang, S. D. Latif, S. Abdullah, M. Ismail, A. El-Shafie. Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia. International Journal of Environmental Science and Technology. 2021; 18 (4):1009-1018.
Chicago/Turabian StyleA. Najah; F. Y. Teo; M. F. Chow; Y. F. Huang; S. D. Latif; S. Abdullah; M. Ismail; A. El-Shafie. 2021. "Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia." International Journal of Environmental Science and Technology 18, no. 4: 1009-1018.
Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2–4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.
Ellysia Jumin; Faridah Bte Basaruddin; Yuzainee Bte. Md Yusoff; Sarmad Dashti Latif; Ali Najah Ahmed. Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia. Environmental Science and Pollution Research 2021, 28, 26571 -26583.
AMA StyleEllysia Jumin, Faridah Bte Basaruddin, Yuzainee Bte. Md Yusoff, Sarmad Dashti Latif, Ali Najah Ahmed. Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia. Environmental Science and Pollution Research. 2021; 28 (21):26571-26583.
Chicago/Turabian StyleEllysia Jumin; Faridah Bte Basaruddin; Yuzainee Bte. Md Yusoff; Sarmad Dashti Latif; Ali Najah Ahmed. 2021. "Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia." Environmental Science and Pollution Research 28, no. 21: 26571-26583.
The purpose of this study is to optimize costs by analyzing a case study of the Energy Utility Company (EUC) project in Malaysia and building the main electric distribution station (MEDS) project specifically. In order to achieve this objective, Value Engineering (VE) technique as one of the proven tools was selected, and three alternatives were applied. The first alternative was to reduce the room sizes of the building. The second alternative was to replace the plaster painting with normal painting, and the last one was to replace painting up to 1.5 meters with 1 meter of the high of walls from the floor. Results approved that the implementation of VE has successfully reduced the cost of the project without compromising the quality of the materials. For the first alternative, 17.1% of the cost was saved. Regarding the second alternative, 69.8% of the total cost for ceiling painting was saved, and for the third alternative, 41.6% of the total cost of walls from the floor was saved. The findings of this research may serve as a guide for engineers, scholars, and constructors to reduce the cost of the building project.
Sarmad Dashti Latif; Fathoni Usman; Bilal M. Pirot. Implementation of Value Engineering in Optimizing Project Cost for Sustainable Energy Infrastructure Asset Development. International Journal of Sustainable Development and Planning 2020, 15, 1045 -1057.
AMA StyleSarmad Dashti Latif, Fathoni Usman, Bilal M. Pirot. Implementation of Value Engineering in Optimizing Project Cost for Sustainable Energy Infrastructure Asset Development. International Journal of Sustainable Development and Planning. 2020; 15 (7):1045-1057.
Chicago/Turabian StyleSarmad Dashti Latif; Fathoni Usman; Bilal M. Pirot. 2020. "Implementation of Value Engineering in Optimizing Project Cost for Sustainable Energy Infrastructure Asset Development." International Journal of Sustainable Development and Planning 15, no. 7: 1045-1057.
Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.
Sarmad Dashti Latif; Muhammad Shukri Bin Nor Azmi; Ali Najah Ahmed; Chow Ming Fai; Ahmed El-Shafie. Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan. International Journal of Design & Nature and Ecodynamics 2020, 15, 647 -652.
AMA StyleSarmad Dashti Latif, Muhammad Shukri Bin Nor Azmi, Ali Najah Ahmed, Chow Ming Fai, Ahmed El-Shafie. Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan. International Journal of Design & Nature and Ecodynamics. 2020; 15 (5):647-652.
Chicago/Turabian StyleSarmad Dashti Latif; Muhammad Shukri Bin Nor Azmi; Ali Najah Ahmed; Chow Ming Fai; Ahmed El-Shafie. 2020. "Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan." International Journal of Design & Nature and Ecodynamics 15, no. 5: 647-652.
Developing water losses and reservoir final storage forecast has become an increasingly important task for reservoir operation. Accurate forecasts would lead to better monitoring of water quality and more efficient reservoir operation. Therefore, the flash flood and water crisis problems in Malaysia can be reduced. Artificial neural networks (ANN) models with radial basis function (RBF) have been determined for high efficiency and accuracy, especially in the dynamics system. In this study, the proposed ANN Prediction Model is being developed by using inflow, the release of dam, initial and final storage of the reservoir as input, whereas the water losses from the reservoir as output. All the data collected over 11 years (1997–2007) at Klang Gate reservoir has been used to develop and test model output. The results indicated that the proposed model could provide monthly forecasting with maximum root mean square error of ± 20.07%. The advantages of this ANN model are to provide information for water losses, final storage, and variation of water level for better reservoir operation.
Sarmad Dashti Latif; Ali Najah Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Engineering Journal 2020, 60, 1365 -1378.
AMA StyleSarmad Dashti Latif, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie. Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Engineering Journal. 2020; 60 (1):1365-1378.
Chicago/Turabian StyleSarmad Dashti Latif; Ali Najah Ahmed; Mohsen Sherif; Ahmed Sefelnasr; Ahmed El-Shafie. 2020. "Reservoir water balance simulation model utilizing machine learning algorithm." Alexandria Engineering Journal 60, no. 1: 1365-1378.
The infiltration process during irrigation is an essential variable for better water management and hence there is a need to develop an accurate model to estimate the amount infiltration water during irrigation. However, the fact that the infiltration process is a highly non-linear procedure and hence required special modeling approach to accurately mimic the infiltration procedure. Therefore, the ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated water during irrigation in the furrow for sustainable management is proposed. The main innovation of current research is the first attempt to employ the ANFIS model for predicating infiltration rates, in addition, integrate the ANFIS model with three new optimization algorithms. Three optimizing algorithms, viz. Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) were used to tune the ANFIS-parameters. Experimental data from six different studies in different countries have been used in this study to validate the proposed model. The inflow rate, furrow length, infiltration opportunity time, cross-sectional area, and waterfront advance time have been utilized as the input parameters. The results indicated that the ANFIS-SCA could provide a better estimation for the infiltration rate compared to ANFIS-PSO. The Mean Absolute Error (MAE) and Percent Bias (PBIAS) errors computed for the ANIFS-SCA (0.007 m3/m and 0.12) was significantly better than those achieved from the ANFIS-FFA and the ANFIS-PSO In addition to that, ANIFS-SCA model outperformed ANFIS-FFA with high level of accuracy. The proposed Hybrid ANFIS-SCA showed outstanding performance over the other optimizer algorithms in estimating the infiltration rate and could be applied in different irrigation systems for better sustainable irrigation management.
Mohammad Ehteram; Fang Yenn Teo; Ali Najah Ahmed; Sarmad Dashti Latif; Yuk Feng Huang; Osama Abozweita; Nadhir Al-Ansari; Ahmed El-Shafie. Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms. Ain Shams Engineering Journal 2020, 12, 1665 -1676.
AMA StyleMohammad Ehteram, Fang Yenn Teo, Ali Najah Ahmed, Sarmad Dashti Latif, Yuk Feng Huang, Osama Abozweita, Nadhir Al-Ansari, Ahmed El-Shafie. Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms. Ain Shams Engineering Journal. 2020; 12 (2):1665-1676.
Chicago/Turabian StyleMohammad Ehteram; Fang Yenn Teo; Ali Najah Ahmed; Sarmad Dashti Latif; Yuk Feng Huang; Osama Abozweita; Nadhir Al-Ansari; Ahmed El-Shafie. 2020. "Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms." Ain Shams Engineering Journal 12, no. 2: 1665-1676.
There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.
Mohammad Ehteram; Ali Najah Ahmed; Sarmad Dashti Latif; Yuk Feng Huang; Meysam Alizamir; Ozgur Kisi; Cihan Mert; Ahmed El-Shafie. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environmental Science and Pollution Research 2020, 28, 1596 -1611.
AMA StyleMohammad Ehteram, Ali Najah Ahmed, Sarmad Dashti Latif, Yuk Feng Huang, Meysam Alizamir, Ozgur Kisi, Cihan Mert, Ahmed El-Shafie. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environmental Science and Pollution Research. 2020; 28 (2):1596-1611.
Chicago/Turabian StyleMohammad Ehteram; Ali Najah Ahmed; Sarmad Dashti Latif; Yuk Feng Huang; Meysam Alizamir; Ozgur Kisi; Cihan Mert; Ahmed El-Shafie. 2020. "Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction." Environmental Science and Pollution Research 28, no. 2: 1596-1611.
Sarmad Dashti Latif. Technical Improvement of Air Pollution Through Fossil Power Plant Waste Management. International Journal of Engineering and Manufacturing 2020, 10, 43 -53.
AMA StyleSarmad Dashti Latif. Technical Improvement of Air Pollution Through Fossil Power Plant Waste Management. International Journal of Engineering and Manufacturing. 2020; 10 (4):43-53.
Chicago/Turabian StyleSarmad Dashti Latif. 2020. "Technical Improvement of Air Pollution Through Fossil Power Plant Waste Management." International Journal of Engineering and Manufacturing 10, no. 4: 43-53.
Vivien Lai; Marlinda Malek; Samsuri Abdullah; Sarmad Dashti Latif; Ali Ahmed. Time-Series Prediction of Sea Level Change in the East Coast of Peninsular Malaysia from the Supervised Learning Approach. International Journal of Design & Nature and Ecodynamics 2020, 15, 409 -415.
AMA StyleVivien Lai, Marlinda Malek, Samsuri Abdullah, Sarmad Dashti Latif, Ali Ahmed. Time-Series Prediction of Sea Level Change in the East Coast of Peninsular Malaysia from the Supervised Learning Approach. International Journal of Design & Nature and Ecodynamics. 2020; 15 (3):409-415.
Chicago/Turabian StyleVivien Lai; Marlinda Malek; Samsuri Abdullah; Sarmad Dashti Latif; Ali Ahmed. 2020. "Time-Series Prediction of Sea Level Change in the East Coast of Peninsular Malaysia from the Supervised Learning Approach." International Journal of Design & Nature and Ecodynamics 15, no. 3: 409-415.
Highway engineering standards are constantly improving. In this study, a design has been done for a horizontal and vertical alignment for the centerline of a federal highway. The construction cost and pavement design have been done according to the Malaysian public works department for the federal roads system (JKR). The results indicate that the designed highway has an adequate, accurate, and economical system based on the proposed data. Accordingly, the objective has been achieved since the reliability of the designed highway was 94%.
Sarmad Dashti Latif. Design of Horizontal and Vertical Alignment for the Centerline of a Federal Highway. International Journal of Engineering and Manufacturing 2020, 10, 27 -42.
AMA StyleSarmad Dashti Latif. Design of Horizontal and Vertical Alignment for the Centerline of a Federal Highway. International Journal of Engineering and Manufacturing. 2020; 10 (3):27-42.
Chicago/Turabian StyleSarmad Dashti Latif. 2020. "Design of Horizontal and Vertical Alignment for the Centerline of a Federal Highway." International Journal of Engineering and Manufacturing 10, no. 3: 27-42.
In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.
Mohammad Ehteram; Ali Najah Ahmed; Lloyd Ling; Chow Ming Fai; Sarmad Dashti Latif; Haitham Abdulmohsin Afan; Fatemeh Barzegari Banadkooki; Ahmed El-Shafie. Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm. Water 2020, 12, 902 .
AMA StyleMohammad Ehteram, Ali Najah Ahmed, Lloyd Ling, Chow Ming Fai, Sarmad Dashti Latif, Haitham Abdulmohsin Afan, Fatemeh Barzegari Banadkooki, Ahmed El-Shafie. Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm. Water. 2020; 12 (3):902.
Chicago/Turabian StyleMohammad Ehteram; Ali Najah Ahmed; Lloyd Ling; Chow Ming Fai; Sarmad Dashti Latif; Haitham Abdulmohsin Afan; Fatemeh Barzegari Banadkooki; Ahmed El-Shafie. 2020. "Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm." Water 12, no. 3: 902.