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In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.
Reventheran Ganasan; Chee Ghuan Tan; Zainah Ibrahim; Fadzli Mohamed Nazri; Muhammad M. Sherif; Ahmed El-Shafie. Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning. Applied Sciences 2021, 11, 7700 .
AMA StyleReventheran Ganasan, Chee Ghuan Tan, Zainah Ibrahim, Fadzli Mohamed Nazri, Muhammad M. Sherif, Ahmed El-Shafie. Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning. Applied Sciences. 2021; 11 (16):7700.
Chicago/Turabian StyleReventheran Ganasan; Chee Ghuan Tan; Zainah Ibrahim; Fadzli Mohamed Nazri; Muhammad M. Sherif; Ahmed El-Shafie. 2021. "Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning." Applied Sciences 11, no. 16: 7700.
Copper is an essential material for electrical conductivity and is a good conductor for heat. The porphyry copper deposits (PCD) are one of the most important resources of copper, where the determination of copper grade is one of the most important issues. The finding complex relationship between copper grade and kind of rocks is a major change for modelers. This study employed the adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) to estimate the copper grade in PCDs. The Henry gas solubility optimization (HGSO), weed algorithm (WA), and moth flame optimization (MFO) were applied to set the parameters of the MLP and ANFIS models. The Iju PCD, as one of the important copper deposits in the Kerman province of Iran, was chosen as a case study for predicting the copper grade. Three scenarios were used as input to the models. The first scenario used the latitude and altitude of boreholes as input and the second scenario used the longitude and altitude of boreholes as input. The third scenario used the latitude, longitude, and altitude of boreholes as input. Results of the first scenario indicated that the percent bias of the ANFIS model was 0.26, while it was 0.19, 0.22, and 0.24 for the ANFIS-HGSO, ANFIS-MFO, and ANFIS-WA models. The accuracy of models indicated that the integration of ANFIS and HGSO decreased the root mean square error (RMSE)of the ANFIS-MFO, ANFIS-WA, and ANFIS models about 14%, 21%, and 27%, respectively, in the training phase in the second scenario. The RMSE for the ANFIS-HGSO was 1.98 in the training phase, while it was 2.31, 2.45, and 2.67 for the ANFIS-MFO, ANFIS-WA, and ANFIS models, respectively, in the third scenario. The accuracy of three input scenarios was compared with that of ANFIS-HSGO. The Mean absolute error of ANFIS-HSGO for the third input scenario was 67% and 40% less than for the first and second input scenarios in the testing phase. The third scenario was the best input scenario. Uncertainty analysis for all the models showed that the least value of uncertainty belonged to the ANFIS-HGSO. This study also used an inclusive multiple model to estimate copper grade based on providing a synergy among multiple models. The utilization of an inclusive multiple model based on the outputs of the hybrid and standalone ANFIS and MLP models could increase the accuracy of individual models. The inclusive multiple model and the comprehensive uncertainty analysis are the innovations of the current study.
Maliheh Abbaszadeh; Mohammad Ehteram; Ali Najah Ahmed; Vijay P. Singh; Ahmed Elshafie. The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization. Earth Science Informatics 2021, 1 -27.
AMA StyleMaliheh Abbaszadeh, Mohammad Ehteram, Ali Najah Ahmed, Vijay P. Singh, Ahmed Elshafie. The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization. Earth Science Informatics. 2021; ():1-27.
Chicago/Turabian StyleMaliheh Abbaszadeh; Mohammad Ehteram; Ali Najah Ahmed; Vijay P. Singh; Ahmed Elshafie. 2021. "The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization." Earth Science Informatics , no. : 1-27.
Floods are the most frequent type of natural disaster. It destroys wildlife habitat, damages bridges, railways, roads, properties, and puts millions of people at risk. As such, flood detection systems have been developed to monitor the changes of water level and raise an alarm should there be imminent danger. River water level prediction is a significant task in flood mitigation planning and floodplains management. Usually, using raw data of rainfall series directly with machine learning (ML) regression methods, does not result in sufficiently good prediction accuracy. The raw data should be pre-processed using specific techniques to enhance their quality a priori to being applied to the prediction methods. This paper serves to address the stated problem by utilizing various data pre-processing techniques such as the Variational Mode Decomposition (VMD), Bagging, Boosting, Bagging-VMD, and Boosting-VMD to enhance the quality of input data and thus culminating in improved model accuracy. The five proposed pre-processing techniques were applied to the observed daily rainfall series of the Dungun river basin, Malaysia, for the period starting from November to February (Northeast Monsoon) from 1996 to 2016. Two machine learning models, the base models (Ori), that is the artificial neural network (ANN) and the support vector regression (SVR), were used in conjunction with the data pre-processing methods. The comparison between the ML methods with and without data pre-processing was done. It was found that prediction of water levels with the two ML methods of SVR and ANN together with the Boosting-VMD was superior to those results derived with just the base original model (Ori). The advantage of the enhanced models (respectively, founded on SVR and ANN) over the original models (SVR and ANN) is best reflected in the performance statistics. Numerical results in terms of root mean square error (RMSE) of (0.42, 0.20 vs 1.85,1.82), mean absolute percentage error (MAPE) of (4.36, 2.82 vs 18.89, 22.56), mean absolute error (MAE) of (0.28,0.16 vs 1.25, 1.41), and Nash–Sutcliffe efficiency coefficient (NSE) (0.96, 0.99 vs 0.25, 0.27) were obtained for the respective models. Additionally, various data visualization graphs such as hydrographs, residual hydrographs, peak-estimates, and box and whisker plots were illustrated to compare between various data pre-processing techniques. The experimental results showed that both the Boosting and the Boosting-VMD methods showed better performance over the other techniques. The Boosting-ANN model was found to be the better model to predict river water levels with the lowest RMSE (0.19), MAPE (2.72), and MAE (0.15) and the highest NSE (0.99).
Ervin Shan Khai Tiu; Yuk Feng Huang; Jing Lin Ng; Nouar AlDahoul; Ali Najah Ahmed; Ahmed Elshafie. An evaluation of various data pre-processing techniques with machine learning models for water level prediction. Natural Hazards 2021, 1 -33.
AMA StyleErvin Shan Khai Tiu, Yuk Feng Huang, Jing Lin Ng, Nouar AlDahoul, Ali Najah Ahmed, Ahmed Elshafie. An evaluation of various data pre-processing techniques with machine learning models for water level prediction. Natural Hazards. 2021; ():1-33.
Chicago/Turabian StyleErvin Shan Khai Tiu; Yuk Feng Huang; Jing Lin Ng; Nouar AlDahoul; Ali Najah Ahmed; Ahmed Elshafie. 2021. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction." Natural Hazards , no. : 1-33.
It is crucial to keep an eye on the water levels in reservoirs in order for them to perform at peak, as they are one of the, if not, the most vital part in water resource management. The water stored is essential in providing water supply, generating hydropower as well as preventing overlasting droughts. Thus, efficient forecasting models are essential in overcoming the issues revolving around hydropower reservoir stations. This paper reviewed the previous research on application of machine learning techniques in forecasting water level in reservoirs. In this review, the discussed machine learning techniques are ANN, ANFIS, BA, COA, SVM, etc., and their main benefits, as well as the literature, are the main focus. Initially, a general study regarding the fundamentals of the respective methods were made. Furthermore, the affecting conditions of water level forecasting, as well as the common issues faced, was also identified, in order to achieve the best results. The advantages and distadvatanges of the algorithms are extracted. In conclusion, hybrid metaheuristic algorithm produced more efficient results. This review paper covered researches conducted from the year 2000 to 2020.
Wei Joe Wee; Nur’Atiah Binti Zaini; Ali Najah Ahmed; Ahmed El-Shafie. A review of models for water level forecasting based on machine learning. Earth Science Informatics 2021, 1 -22.
AMA StyleWei Joe Wee, Nur’Atiah Binti Zaini, Ali Najah Ahmed, Ahmed El-Shafie. A review of models for water level forecasting based on machine learning. Earth Science Informatics. 2021; ():1-22.
Chicago/Turabian StyleWei Joe Wee; Nur’Atiah Binti Zaini; Ali Najah Ahmed; Ahmed El-Shafie. 2021. "A review of models for water level forecasting based on machine learning." Earth Science Informatics , no. : 1-22.
Accurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essential. Thus, this work recommends various supervised machine learning algorithms for predicting water levels with groundwater level correlation. The predicting models have Linear Regression (LR), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Neural Network (NN). This study includes four scenarios; The first scenario (SC1) uses lag 1; second scenario (SC2) uses lag 1 and lag 2; third scenario (SC3) uses lag 1, lag 2, and lag 11 and the fourth scenario (SC4) uses lag 1, lag 2, lag 11 and lag 12. These scenarios have been determined using the autocorrelation function (ACF), and these lags represent the month. The results showed that for SC1, SC2, and SC4, all model performance in GPR gave good results where the highest R equal to 0.71 in SC1, 0.78 in SC2, and 0.73 in SC4 using the Matern 5/2 GPR model. For SC3, the Stepwise LR model gave a better result with an R of 0.79. It can be concluded that Matern 5/2 of Gaussian Processes Regression Models is a reliable model to predict water level as the method gave a high performance in each scenario (except SC3) with a relatively fastest training time. The NN model had the worst performance to the other three models since it has the highest MAE values, RMSE, and lowest value of R in almost all four scenarios of input combinations. These results obtained in this study serves as an excellent benchmark for future water level prediction using the GPR and LR with four scenarios created.
Michelle Sapitang; Wanie M. Ridwan; Ali Najah Ahmed; Chow Ming Fai; Ahmed El-Shafie. Groundwater level as an input to monthly predicting of water level using various machine learning algorithms. Earth Science Informatics 2021, 14, 1269 -1283.
AMA StyleMichelle Sapitang, Wanie M. Ridwan, Ali Najah Ahmed, Chow Ming Fai, Ahmed El-Shafie. Groundwater level as an input to monthly predicting of water level using various machine learning algorithms. Earth Science Informatics. 2021; 14 (3):1269-1283.
Chicago/Turabian StyleMichelle Sapitang; Wanie M. Ridwan; Ali Najah Ahmed; Chow Ming Fai; Ahmed El-Shafie. 2021. "Groundwater level as an input to monthly predicting of water level using various machine learning algorithms." Earth Science Informatics 14, no. 3: 1269-1283.
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.
Solid Waste (SW) is one of the critical challenges of urban life. These SWs are considered environmental pollutants that are a threat to ecology and human health. Predicting SW generation is an essential topic for scholars to better manage SWs. This study investigates the application of optimised ANN models for predicting monthly SW generation in Iran using datasets about seven Iranian megacities. The Archimedes Optimisation Algorithm (AOA), Sine Cosine Algorithm (SCA), Particle Swarm Optimisation (PSO) technique, and Genetic Algorithms (GA) were used for training the ANN model. The enhanced gamma test was used to determine the best input combination. AOA and the gamma test were used concurrently to reduce the time needed for choosing the best input combination. Gross domestic product (GDP), population, household size, and numbers of months were the best input combination set. This best input combination was then inputted into the hybrid and standalone ANN models for predicting monthly SW generation. During the final phase, the outputs of ANN-AOA, ANN-SCA, ANN-PSO, ANN-GA, and ANN models were used as inputs for an inclusive multiple model (IMM) in order to enhance model accuracy. The IMM model reduced the training phase root mean square error (RMSE) of ANN-AOA, ANN-SCA, ANN-PSO, ANN-GA, and ANN models by 55%, 59%, 68%, 72%, and 73%, respectively. Although ANN-AOA provided higher R2 and lower RMSE values than ANN-PSO, ANN-SCA, ANN-GA and ANN models, the IMM model outperformed ANN-AOA, considering that it integrates the advantages of all models used in the current study. The current study also used the fuzzy reasoning concept for modifying ANN model structures. The results indicated that such ANN models' time requirement was lower than those without fuzzy reasoning concept. The general results of the current study indicate that the ANN-AOA and the fuzzy-reasoning based Inclusive Multiple Model have a high ability for predicting different target variables.
Guoxi Liang; Fatemeh Panahi; Ali Najah Ahmed; Mohammad Ehteram; Shahab S. Band; Ahmed Elshafie. Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components. Journal of Cleaner Production 2021, 315, 128039 .
AMA StyleGuoxi Liang, Fatemeh Panahi, Ali Najah Ahmed, Mohammad Ehteram, Shahab S. Band, Ahmed Elshafie. Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components. Journal of Cleaner Production. 2021; 315 ():128039.
Chicago/Turabian StyleGuoxi Liang; Fatemeh Panahi; Ali Najah Ahmed; Mohammad Ehteram; Shahab S. Band; Ahmed Elshafie. 2021. "Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components." Journal of Cleaner Production 315, no. : 128039.
Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi-reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation. To achieve this, the Two-Point Heading Rule was integrated with a new optimization algorithm, namely the Seagull Optimization Algorithm (SEOA). The Two-Point Heading Rule was used based on four distinct scenarios, namely Two-Point Heading Rule (1), Two-Point Heading Rule (2), Two-Point Heading Rule (3) and Two-Point Heading Rule (4). The Seagull Optimization Algorithm was then used to find two heading parameters of the TPHRs. The Seagull Optimization Algorithm was subsequently benchmarked against the Salp Swarm Algorithm (SSA), Bat Algorithm (BA) and the Shark Optimization Algorithm (SOA). Various inflow scenarios consisting of the first inflow scenario (dry condition), the second inflow scenario (normal) and the third inflow scenario (wet condition) were considered for the optimal operation of this multi-reservoir system. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the first inflow scenario and was 3.22 while the average solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 3.25, 3.93, 4.87 and 6.03. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the second inflow scenario was 2.14 while the average best solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 2.16, 2.98, 3.96, and 4.89. It can be concluded that the SEOA outperformed all of the other algorithms. It was also found that the SEOA based on the Two-Point Heading Rule (3) under the third inflow scenario provided the most power generation for the KHI and KAIV systems. A multi-criteria decision was utilized to choose the best algorithm and heading policy. The ensuing results indicate that the SEOA had the best performance out of all the algorithms based on Two-Point Heading Rule (3) and the third inflow scenario.
Mohammad Ehteram; Fatemeh Barzegari Banadkooki; Chow Ming Fai; Mohsen Moslemzadeh; Michelle Sapitang; Ali Najah Ahmed; Dani Irwan; Ahmed El-Shafie. Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy. Energy Reports 2021, 7, 3703 -3725.
AMA StyleMohammad Ehteram, Fatemeh Barzegari Banadkooki, Chow Ming Fai, Mohsen Moslemzadeh, Michelle Sapitang, Ali Najah Ahmed, Dani Irwan, Ahmed El-Shafie. Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy. Energy Reports. 2021; 7 ():3703-3725.
Chicago/Turabian StyleMohammad Ehteram; Fatemeh Barzegari Banadkooki; Chow Ming Fai; Mohsen Moslemzadeh; Michelle Sapitang; Ali Najah Ahmed; Dani Irwan; Ahmed El-Shafie. 2021. "Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy." Energy Reports 7, no. : 3703-3725.
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.
The massive destruction and loss caused by the 2004 Sumatra–Andaman tsunami were attributed to the lack of knowledge on tsunami and low regional detection and communication systems for early warning in that region. This study aimed to identify locations at risk of impending tsunami from Andaman Sea for the safety of community and proper development planning at the coastal areas by providing an updated and revised inundation maps. The last study on this area was conducted several years ago which open the possibility to new findings. Generated by tsunami simulation models, the maps illustrate the extent and level of inundation to which the coastal community and infrastructure would be subjected. As a result of coastal changes and availability of better topographic data, the existing inundation maps for the coastal areas of northwest Peninsular Malaysia at risk to impending tsunami from the Andaman Sea are revised. This paper documented the computational setup leading to the generation of the revised inundation maps. The tsunami simulation model TUNA was used to simulate the generation, propagation, and subsequent run-up and inundation of tsunamis triggered by earthquakes of moment magnitudes (Mw) 8.5, 9.0, and 9.25 along the Sunda Trench. From the simulations, it was found that at Mw 9.25, Balik Pulau, Pulau Pinang would be subjected to inundation of as far as 3.47 km with 5.40-m-deep inundation at the highest section.
Nurul Natasha Nabila Naim; Nurul Hani Mardi; Marlinda Abdul Malek; Su Yean Teh; Mohd Azwan Wil; Abd Halim Shuja; Ali Najah Ahmed. Tsunami inundation maps for the northwest of Peninsular Malaysia and demarcation of affected electrical assets. Environmental Monitoring and Assessment 2021, 193, 1 -17.
AMA StyleNurul Natasha Nabila Naim, Nurul Hani Mardi, Marlinda Abdul Malek, Su Yean Teh, Mohd Azwan Wil, Abd Halim Shuja, Ali Najah Ahmed. Tsunami inundation maps for the northwest of Peninsular Malaysia and demarcation of affected electrical assets. Environmental Monitoring and Assessment. 2021; 193 (7):1-17.
Chicago/Turabian StyleNurul Natasha Nabila Naim; Nurul Hani Mardi; Marlinda Abdul Malek; Su Yean Teh; Mohd Azwan Wil; Abd Halim Shuja; Ali Najah Ahmed. 2021. "Tsunami inundation maps for the northwest of Peninsular Malaysia and demarcation of affected electrical assets." Environmental Monitoring and Assessment 193, no. 7: 1-17.
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).
Karim Sherif Mostafa Hassan Ibrahim; Yuk Feng Huang; Ali Najah Ahmed; Chai Hoon Koo; Ahmed El-Shafie. A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alexandria Engineering Journal 2021, 61, 279 -303.
AMA StyleKarim Sherif Mostafa Hassan Ibrahim, Yuk Feng Huang, Ali Najah Ahmed, Chai Hoon Koo, Ahmed El-Shafie. A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alexandria Engineering Journal. 2021; 61 (1):279-303.
Chicago/Turabian StyleKarim Sherif Mostafa Hassan Ibrahim; Yuk Feng Huang; Ali Najah Ahmed; Chai Hoon Koo; Ahmed El-Shafie. 2021. "A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting." Alexandria Engineering Journal 61, no. 1: 279-303.
Earthquakes have been universally recognised as seismological disasters that pose a threat to civilization and need to be monitored through prediction models. The development and usage of traditional statistical predicting models, which require the understanding of underlying physical scientific processes in a system and large amounts of data preparation, can be challenging and costly. Artificial intelligence-based models, specifically machine learning models, are able to easily review mass data volumes and identify complex data trends to make predictions, making them beneficial to be utilized as prediction models. Terengganu, located on the east coast of Peninsular Malaysia, has experienced three earthquakes in the last four decades and has the potential to be hit or affected by earthquakes due to its location within the vicinity of the South China Sea where the seismologically active Manila Trench is situated. This makes the development of machine learning models for the prediction of earthquakes in Terengganu important for future disaster analysis and management. Therefore, this study suggests artificial neural network (ANN) models as a tool to predict ground motion parameters, namely earthquake acceleration, depth, and velocity, in Terengganu. However, this study presents the comparison of the results of ANN with the results of Random Forest (RF). The data used to develop the models were collected by six seismological stations for two channels in Terengganu and provided by the Malaysian Meteorological Department. The data was partitioned into six sets for each ground motion parameter in each channel, with each set utilizing data from a different grouping of five stations for training and one station for testing. Earthquake depth was able to be modelled with accuracy univariately, that is using only the respective output parameter, which is earthquake depth, as the input parameter. Earthquake acceleration and velocity could not be modelled with accuracy univariately, and were improved by adding earthquake depth as an input parameter. Based on the analysis and evaluation of the results using four selected performance criteria, the ANN models show good performance in predicting earthquake acceleration, depth, and velocity.
Yusuf Essam; Pavitra Kumar; Ali Najah Ahmed; Muhammad Ary Murti; Ahmed El-Shafie. Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia. Soil Dynamics and Earthquake Engineering 2021, 147, 106826 .
AMA StyleYusuf Essam, Pavitra Kumar, Ali Najah Ahmed, Muhammad Ary Murti, Ahmed El-Shafie. Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia. Soil Dynamics and Earthquake Engineering. 2021; 147 ():106826.
Chicago/Turabian StyleYusuf Essam; Pavitra Kumar; Ali Najah Ahmed; Muhammad Ary Murti; Ahmed El-Shafie. 2021. "Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia." Soil Dynamics and Earthquake Engineering 147, no. : 106826.
Nanotechnologies present a promising application in the production of water to obtain potable water from natural sources by treatment. Clinoptilolite is an abundant and low-cost natural zeolite, which has adsorbent properties. Moreover, this study introduces a simple method for non-harmful natural adsorbent preparation and modification using different acids and bases with convenient and accessible desorption. The characterization methods used in this study are X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Response Surface Method (RSM) has been utilized to reflect the influence of operations variables in Design Expert 7.0. Other Variables, including the Effect of Modifications, contact time, the percentage of the dosage of adsorbent to the beginning (initial) pollutants concentration (D/C), and pH on total iron, were researched during the research procedure. The characterization results indicated that clinoptilolite nanoparticles were well prepared, and the preparation method was effective. Best modification Performance was shown in Z10(0.1 M Hydrochloric Acid dried by Air) at 24 h Setting time. The achieved results from Design Expert showed that at D/C and pH equal to 81.53 and 6.5, respectively, the optimal percentage of the iron removal is obtained with a value equal to 97.97%. In addition, Freundlich isothermal model best described the total iron adsorption process, and it has been observed that the maximum adsorption capacity is 50 mg/g for the total iron removal.
Amir Hossein Salimi; Ali Shamshiri; Ehsan Jaberi; Hossein Bonakdari; Azam Akhbari; Robert Delatolla; Mohammad Reza Hassanvand; Mohammad Agharazi; Yuk Feng Huang; Ali Najah Ahmed; Ahmed Elshafie. Total iron removal from aqueous solution by using modified clinoptilolite. Ain Shams Engineering Journal 2021, 1 .
AMA StyleAmir Hossein Salimi, Ali Shamshiri, Ehsan Jaberi, Hossein Bonakdari, Azam Akhbari, Robert Delatolla, Mohammad Reza Hassanvand, Mohammad Agharazi, Yuk Feng Huang, Ali Najah Ahmed, Ahmed Elshafie. Total iron removal from aqueous solution by using modified clinoptilolite. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleAmir Hossein Salimi; Ali Shamshiri; Ehsan Jaberi; Hossein Bonakdari; Azam Akhbari; Robert Delatolla; Mohammad Reza Hassanvand; Mohammad Agharazi; Yuk Feng Huang; Ali Najah Ahmed; Ahmed Elshafie. 2021. "Total iron removal from aqueous solution by using modified clinoptilolite." Ain Shams Engineering Journal , no. : 1.
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.
Sediment deposition causes the reduction of aquatic habitats and increase of water velocities within rivers, which negatively impacts the environment and the surrounding ecology. This makes the prediction of river sediment deposition a key factor for the protection of river environments. The prediction of sediment deposition in rivers through the integration of satellite imagery and unsupervised machine learning is beneficial and convenient, as it is less resource-intensive due to not requiring ground-truth data. The Terengganu River in Malaysia is used as a case study in this research. This study aims to discuss satellite imagery's key preparation processes, namely image correction and identification of determinant image bands through a correlation analysis. Satellite imagery of the Terengganu River between 1989 and 2019 is obtained from the United States Geological Survey (USGS). Image correction is successfully implemented on the available satellite imagery with the results shown in this study. Through the performed correlation analysis, the study finds that the determinant image bands for river sediment deposition prediction using unsupervised machine learning are the NST spectral bands, which consist of the NIR, SWIR, and TIR bands. This is due to the NST spectral bands exhibiting low correlations with respect to the RGB bands. It is found that correlation coefficients between the NIR band and red, green, and blue bands are generally the lowest, especially in 2009 with values of 0.1087, 0.2085, and 0.1252, respectively. This indicates that the NIR band is the most important determinant image band in predicting river sediment deposition. This study also identifies k-means, clustering large application (Clara), and hierarchical agglomerative clustering (HAC) as suitable unsupervised machine learning algorithms to be utilized in predicting river sediment deposition. Studies on the application of unsupervised machine learning algorithms on satellite imagery in the field of river sediment deposition prediction are currently scarce, possibly due to the gap of knowledge on the initial steps required for such application. Therefore, this study's novelty is the introduction and discussion on critical preliminary processes, specifically image correction and identification of determinant image bands, that are required for the successful implementation of unsupervised machine learning algorithms on satellite imagery for the prediction of river sediment deposition.
Awatif Aziz; Yusuf Essam; Ali Najah Ahmed; Yuk Feng Huang; Ahmed El-Shafie. An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery. Ain Shams Engineering Journal 2021, 1 .
AMA StyleAwatif Aziz, Yusuf Essam, Ali Najah Ahmed, Yuk Feng Huang, Ahmed El-Shafie. An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleAwatif Aziz; Yusuf Essam; Ali Najah Ahmed; Yuk Feng Huang; Ahmed El-Shafie. 2021. "An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery." Ain Shams Engineering Journal , no. : 1.
Earthquake is one of the devastating and frightening natural disasters that caused big casualties in a small duration. Earthquake caused lots of damage in just a few minutes and the casualties of the earthquake increase as the population increase which also contribute to higher amount of property and buildings. Therefore, by developing model capable of detecting the recurrence behaviour of earthquake helps in predicting earthquake as well as minimizing the casualties caused by the earthquake. In this report, a few of artificial intelligence algorithms such as support vector machine, boosted decision tree regression, random forest and multivariate adaptive regression spline will be used in the development of best model algorithm in earthquake prediction. Meteorological data are collected from several stations in Terengganu and processed for normalization and the data will be analysed using algorithms and its performance will be evaluated. Terengganu is situated on the east coast of Peninsular Malaysia and is bordered on the north-west and south-west by Kelantan and Pahang. Terengganu's east side is bordered by the South China Sea. Terengganu is located within the vicinity of the South China Sea, which is possible to be affected by the Marina Trench Earthquake. The subduction zone of Manila Trench is capable of producing a high magnitude of earthquake activity that can create a deadliest tsunami disaster. Therefore, Terengganu is studied for the investigation of artificial intelligence in earthquake prediction. The model algorithms are then analysed to measure its sensitivity and accuracy in prediction and consistency of the result.
Suzlyana Marhain; Ali Najah Ahmed; Muhammad Ary Murti; Pavitra Kumar; Ahmed El-Shafie. Investigating the application of artificial intelligence for earthquake prediction in Terengganu. Natural Hazards 2021, 108, 977 -999.
AMA StyleSuzlyana Marhain, Ali Najah Ahmed, Muhammad Ary Murti, Pavitra Kumar, Ahmed El-Shafie. Investigating the application of artificial intelligence for earthquake prediction in Terengganu. Natural Hazards. 2021; 108 (1):977-999.
Chicago/Turabian StyleSuzlyana Marhain; Ali Najah Ahmed; Muhammad Ary Murti; Pavitra Kumar; Ahmed El-Shafie. 2021. "Investigating the application of artificial intelligence for earthquake prediction in Terengganu." Natural Hazards 108, no. 1: 977-999.
Hydrological models play a crucial role in water planning and decision making. Machine Learning-based models showed several drawbacks for frequent high and a wide range of streamflow records. These models also experience problems during the training process such as over-fitting or trapping in searching for global optima To overcome these limitations, the current study attempts to hybridize the recently developed physics-inspired metaheuristic algorithms (MHAs) such as Equilibrium Optimization (EO), Henry Gases Solubility Optimization (HGSO), and Nuclear Reaction Optimization(NRO) with Multi-layer Perceptron (MLP). These models’ accuracy will be inspected to solve the streamflow forecasting problem where the streamflow dataset was collected through 130 years from a station located on the High Aswan Dam (HAD). The performance of proposed models then will be compared with two traditional neural network models(MLP and RNN), and nine well-known hybrid MLP-based models belong to the different branches of the metaheuristic field (evolutionary group, swarm group, and physics group). The internal parameters of the proposed models will be initialized and optimized. Different performance metrics will be used to examine the performance of the proposed models. The stability of the proposed models and the convergence speed will be evaluated. Finally, ranking these models based on different performance evaluations will be carried out. The results show that the models in the group of Physics-MLP are more reliable in capturing the streamflow patterns, followed by the Swarm-MLP group and then by the Evolutionary-MLP group. Finally, among the all employed methods, the NRO has the best accuracy with the lowest RMSE(2.35), MAE(1.356), MAPE(16.747), and the highest WI(0.957), R(0.924), and confidence in forecasting the streamflow of Aswan High Dam. It can be concluded that augmenting the NRO algorithm with MLP can be a reliable tool in forecasting the monthly streamflow with a high level of precision, speed convergence, and high constancy level.
Ali Najah Ahmed; To Van Lam; Nguyen Duy Hung; Nguyen Van Thieu; Ozgur Kisi; Ahmed El-Shafie. A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Applied Soft Computing 2021, 105, 107282 .
AMA StyleAli Najah Ahmed, To Van Lam, Nguyen Duy Hung, Nguyen Van Thieu, Ozgur Kisi, Ahmed El-Shafie. A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Applied Soft Computing. 2021; 105 ():107282.
Chicago/Turabian StyleAli Najah Ahmed; To Van Lam; Nguyen Duy Hung; Nguyen Van Thieu; Ozgur Kisi; Ahmed El-Shafie. 2021. "A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem." Applied Soft Computing 105, no. : 107282.
A reliable river water level model to forecast the changes in different lead times is vital for flood warning systems, especially in countries like Malaysia, where flood is considered the most devastating natural disaster. In the current study, the ability of two artificial intelligence (AI) based data-driven approaches: Multi-layer Perceptron Neural Networks (MLP-NN) and An Adaptive Neuro-Fuzzy Inference System (ANFIS), as reliable models in forecasting the river level based on an hourly basis are investigated. 10-year of hourly measured data of the Muda river's water level in the northern part of Malaysia is used for training and testing the proposed models. Different statistical indices are introduced to validate the reliability of the models. Optimizing the hyper-parameters for both models is explored. Then, sensitivity analysis and uncertainty analysis are carried out. Finally, the capability of the models to forecast the river level for different lead times (1, 3, 6, 9, 12, and 24-hours ahead) is investigated. The results reveal that a high accuracy was achieved for the MLP-NN model with 4 hidden neurons with RMSE (0.01740), while for ANFIS, a model with three G-bell shaped membership functions outperformed other ANFIS models with RMSE (0.0174). MLP-NN and ANFIS achieved a high level of performance when two input combinations were used with RMSE equal to 0.01299 and 0.0130, respectively. However, MLP outperformed ANFIS in terms of running time and the uncertainty analysis test, in which the d-factor is found to be 0.000357.
Muhamad Nur Adli Zakaria; Marlinda Abdul Malek; Maslina Zolkepli; Ali Najah Ahmed. Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia. Alexandria Engineering Journal 2021, 60, 4015 -4028.
AMA StyleMuhamad Nur Adli Zakaria, Marlinda Abdul Malek, Maslina Zolkepli, Ali Najah Ahmed. Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia. Alexandria Engineering Journal. 2021; 60 (4):4015-4028.
Chicago/Turabian StyleMuhamad Nur Adli Zakaria; Marlinda Abdul Malek; Maslina Zolkepli; Ali Najah Ahmed. 2021. "Application of artificial intelligence algorithms for hourly river level forecast: A case study of Muda River, Malaysia." Alexandria Engineering Journal 60, no. 4: 4015-4028.
This research studies the implementation of artificial neural networks (ANN) in predicting the concentration of total suspended solids (TSS) for the Fei Tsui reservoir in Taiwan. The prediction model developed in this study is designed to be used for monitoring the water quality in the Fei Tsui reservoir. High concentrations of total suspended solids (TSS) have been a crucial problem in the Fei Tsui reservoir for decades. As the Fei Tsui reservoir is a primary water source for Taipei City, this issue impacts the drinking water supply for the city due to etherification problems in the reservoir. 10-year average monthly records and 13-year average annual records have been collected for 26 parameters and correlated with the TSS concentrations to determine the parameters that have a strong relationship with the TSS concentrations. The parameters that were shown to have a strong correlation with the TSS concentration are the trophic state index (TSI), nitrate (NO3) concentration, total phosphorous (TP) concentration, iron concentration (IRON), and turbidity. Linear regression was used to develop the model that estimates the TSS concentration in the Fei Tsui Reservoir. The results show that model 3, a three-layer ANN model that uses three-input parameters namely NO3 concentration, TP concentration, and turbidity, with five neurons, to predict the output parameter which is TSS concentration, produces the highest coefficient of determination (R2) and Willmott Index (WI), which are 0.9589 and 0.9933 respectively, and the lowest root mean square error, which is 0.4753. Based on these performance criteria, model 3 is concluded as the best model to predict TSS concentrations in this study.
Balahaha Hadi Ziyad Sami; Wong Jee Khai; Chow Ming Fai; Yusuf Essam; Ali Najah Ahmed; Ahmed El-Shafie. Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction. Ain Shams Engineering Journal 2021, 12, 1607 -1622.
AMA StyleBalahaha Hadi Ziyad Sami, Wong Jee Khai, Chow Ming Fai, Yusuf Essam, Ali Najah Ahmed, Ahmed El-Shafie. Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction. Ain Shams Engineering Journal. 2021; 12 (2):1607-1622.
Chicago/Turabian StyleBalahaha Hadi Ziyad Sami; Wong Jee Khai; Chow Ming Fai; Yusuf Essam; Ali Najah Ahmed; Ahmed El-Shafie. 2021. "Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction." Ain Shams Engineering Journal 12, no. 2: 1607-1622.
The prediction of tropospheric ozone concentrations is vital due to ozone’s passive impacts on atmosphere, people’s health, flora and fauna. However, ozone prediction is a complex process and the wide range of traditional models is incapable to obtain an accurate prediction. “Artificial intelligence”, “machine learning” and “ozone prediction model” search terms in the title, abstract or keywords are involved. Inclusion criteria include subject area (engineering, computer science), English language and being published from 2015. This criterion obtained 156 articles, which were categorized into 4 areas of interest based on the machine learning technique applied. Recently as a result of the rapid development in the technology and the increase in the number of measured data, artificial intelligence techniques have been intensively used in predicting ozone concentration as an alternative to the traditional models. Therefore, the main objective of this study is to investigate the most developed techniques that have been used in predicting ozone concentrations as well as theoretic approaches such as information set approaches, fuzzy set approach and probabilistic set approaches. It is clearly stated that the standalone algorithms such as decision tree (DT) and support vector machine (SVM) outperformed multilayer perceptron (MLP); however, the latter is massively implemented by many researchers in the prediction of ozone concentrations. This review paper investigated artificial intelligence techniques integrated with optimization approaches. It can be concluded that hybrid algorithms have significantly improved the prediction accuracy. However, the majority of the proposed hybrid models have limitations; thus, there is a need to develop better hybrid algorithm that is able to tackle all the drawbacks of the improved algorithms and capable to capture the ozone concentration changes with a high level of accuracy.
Ayman Yafouz; Ali Najah Ahmed; Nur’Atiah Zaini; Ahmed El-Shafie. Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review. Water, Air, & Soil Pollution 2021, 232, 1 -29.
AMA StyleAyman Yafouz, Ali Najah Ahmed, Nur’Atiah Zaini, Ahmed El-Shafie. Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review. Water, Air, & Soil Pollution. 2021; 232 (2):1-29.
Chicago/Turabian StyleAyman Yafouz; Ali Najah Ahmed; Nur’Atiah Zaini; Ahmed El-Shafie. 2021. "Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review." Water, Air, & Soil Pollution 232, no. 2: 1-29.