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Dr. Saad Shauket Sammen
Civil Engineering Department - College of Engineering - University of Diyala - Diyala Governorate - Iraq

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0 Hydraulic Modeling
0 Hydrology
0 Water Quality Modeling
0 ground water
0 Water Resources Engineering

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Preprint content
Published: 13 August 2021
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Increased extreme rainfall due to climate change will increase the probable maximum flood (PMF) and pose a severe threat the critical hydraulic infrastructure like hydroelectric and flood protection dams. As the rainfall extremes in tropical regions are highly sensitive to global warming, increase PMF can be much higher in the tropics. A study has been conducted to assess the impact of climate change on PMF in a tropical catchment located in peninsular Malaysia. A lumped hydrological model, Mike NAM, is calibrated and validated with observed climate and inflow data of Tenmengor reservoir, located in the state of Perak of Peninsular Malaysia. Regional climate model projected rainfall is used to generate probable maximum precipitation (PMP) for future periods. The hydrological model is used to simulate PMF from PMP estimated for the historical and two future periods, early (2031−2045) and late (2060−2075). The results revealed the NAM model could simulate the river flow with a Nash–Sutcliffe efficiency of 0.74 and root mean square error of 0.51. The application of the model with projected rainfall revealed an increase in PMP by 162 to 507% and 259 to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the study area due to climate change.

ACS Style

Saad Shauket Sammen; Thamer A Mohammed; Abdul Halim Ghazali; Lariyah M Sidek; Shamsuddin Shahid; Sani Isah Abba; Anurag Malik. Assessment of Climate Change Impact on Probable Maximum Floods in a Tropical Catchment. 2021, 1 .

AMA Style

Saad Shauket Sammen, Thamer A Mohammed, Abdul Halim Ghazali, Lariyah M Sidek, Shamsuddin Shahid, Sani Isah Abba, Anurag Malik. Assessment of Climate Change Impact on Probable Maximum Floods in a Tropical Catchment. . 2021; ():1.

Chicago/Turabian Style

Saad Shauket Sammen; Thamer A Mohammed; Abdul Halim Ghazali; Lariyah M Sidek; Shamsuddin Shahid; Sani Isah Abba; Anurag Malik. 2021. "Assessment of Climate Change Impact on Probable Maximum Floods in a Tropical Catchment." , no. : 1.

Research article
Published: 30 July 2021 in Environmental Science and Pollution Research
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The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.

ACS Style

Mohammad Ehteram; Saad Sh. Sammen; Fatemeh Panahi; Lariyah Mohd Sidek. A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization. Environmental Science and Pollution Research 2021, 1 -22.

AMA Style

Mohammad Ehteram, Saad Sh. Sammen, Fatemeh Panahi, Lariyah Mohd Sidek. A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization. Environmental Science and Pollution Research. 2021; ():1-22.

Chicago/Turabian Style

Mohammad Ehteram; Saad Sh. Sammen; Fatemeh Panahi; Lariyah Mohd Sidek. 2021. "A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization." Environmental Science and Pollution Research , no. : 1-22.

Journal article
Published: 12 July 2021 in Sustainability
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Rainfall and evaporation, which are known as two complex and unclear processes in hydrology, are among the key processes in the design and management of water resource projects. The application of artificial intelligence, in comparison with physical and empirical models, can be effective in the face of the complexity of hydrological processes. The present study was prepared with the aim of increasing the accuracy in monthly prediction of rainfall (R) and pan evaporation (EP) by providing a simple solution to determining new inputs for forecasting scenarios. Initially, the prediction of two parameters, R and EP, for the current and one–three lead times, by determining the different input modes, was developed with the SVM model. Then, in order to increase the accuracy of the predictions, the month number (τ) was added to all scenarios in predicting both the R and EP parameters. The results of the intelligent model using several statistical indices (i.e., root mean square error (RMSE), Kling–Gupta (KGE) and correlation coefficient (CC)), with the help of case visual indicators, were compared. The month number (τ) was able to greatly improve the prediction accuracy of both the R and EP parameters under the SVM model and overcome the complexities within these two hydrological processes that the scenarios were not initially able to solve with high accuracy. This is proven in all time steps. According to the RMSE, KGE and CC indices, the highest increase in the forecast accuracy for the upcoming two months of rainfall (Rt+2) for Ardabil station in scenario 2 (SVM-2) was 19.1, 858 and 125%, and for the current month of pan evaporation (EPt) for Urmia station in scenario 6 (SVM-6), this occurred at the rates of 40.2, 11.1 and 7.6%, respectively. Finally, in order to investigate the characteristic of the month number in the SVM model under special conditions such as considering the highest values of the R and EP time series, it was proved that by using the month number of the SVM model, again, the accuracy could be improved (on average, 17% improvement for rainfall, and 13% for pan evaporation) in almost all time steps. Due to the wide range of effects of the two variables studied in the hydrological discussion, the results of the present study can be useful in agricultural sciences and in water management in general and will help owners.

ACS Style

Ieva Meidute-Kavaliauskiene; Milad Alizadeh Jabehdar; Vida Davidavičienė; Mohammad Ali Ghorbani; Saad Sammen. A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model. Sustainability 2021, 13, 7752 .

AMA Style

Ieva Meidute-Kavaliauskiene, Milad Alizadeh Jabehdar, Vida Davidavičienė, Mohammad Ali Ghorbani, Saad Sammen. A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model. Sustainability. 2021; 13 (14):7752.

Chicago/Turabian Style

Ieva Meidute-Kavaliauskiene; Milad Alizadeh Jabehdar; Vida Davidavičienė; Mohammad Ali Ghorbani; Saad Sammen. 2021. "A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model." Sustainability 13, no. 14: 7752.

Research article
Published: 03 June 2021 in Hydrological Sciences Journal
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Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential of the Emotional Artificial Neural Network-Genetic Algorithm (EANN-GA), and three different ensemble techniques, i.e., Emotional Artificial Neural Network (EANN), Feed Forward Neural Network (FFNN), and Neural Network ensemble (NNE) to predict DO concentration in Kinta River basin of Malaysia. The performance of EANN-GA, EANN, FFNN, and NNE models in predicting DO was evaluated by using statistical metrics and visual interpretation. The appraisal of results revealed promising performance of the NNE-M3 model (Nash-Sutcliffe Efficiency: NSE = 0.8743/ 0.8630, Correlation Coefficient: CC = 0.9351/ 0.9113, Mean Square Error: MSE = 0.5757/ 0.6833 mg/L, Root Mean Square Error: RMSE = 0.7588/ 0.8266 mg/L, and Mean Absolute Percentage Error: MAPE = 20.6581/ 14.1675) during calibration/ validation period compared to EANN-GA, EANN, and FFNN models in DO prediction in the study basin.

ACS Style

S.I. Abba; R.A. Abdulkadir; Saad Sh. Sammen; A.G. Usman; Sarita Gajbhiye Meshram; Anurag Malik; Shamsuddin Shahid. Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration. Hydrological Sciences Journal 2021, 1 .

AMA Style

S.I. Abba, R.A. Abdulkadir, Saad Sh. Sammen, A.G. Usman, Sarita Gajbhiye Meshram, Anurag Malik, Shamsuddin Shahid. Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration. Hydrological Sciences Journal. 2021; ():1.

Chicago/Turabian Style

S.I. Abba; R.A. Abdulkadir; Saad Sh. Sammen; A.G. Usman; Sarita Gajbhiye Meshram; Anurag Malik; Shamsuddin Shahid. 2021. "Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration." Hydrological Sciences Journal , no. : 1.

Original paper
Published: 13 May 2021 in Stochastic Environmental Research and Risk Assessment
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Accurate stream flow quantification and prediction are essential for the local and global planning and management of basins to cope with climate change. The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations. Principal component analysis (PCA) was performed on Q (t) (t: the number of the current day) before model creation to pick essential inputs for a maximum of 6 lags. With the classical MLP and two other hybrid MLP models (MLP-particle swarm optimization (MLP-PSO) and MLP-genetic algorithm (MLP-GA)), the results of the MLP-sunflower algorithm (SFA) were benchmarked. As compared to other models, the MLP-SFA could be able to reduce the Root Mean Square Error (RMSE) by a value of between 12 and 21% at the JSO station and between 8 and 24% at the MDJ station. In conclusion, this research found that combining MLP with optimization algorithms improved the precision of the stand-alone MLP model, with SFA integration being the most efficient.

ACS Style

Saad Sh. Sammen; Mohammad Ehteram; S. I. Abba; R. A. Abdulkadir; Ali Najah Ahmed; Ahmed El-Shafie. A new soft computing model for daily streamflow forecasting. Stochastic Environmental Research and Risk Assessment 2021, 1 -13.

AMA Style

Saad Sh. Sammen, Mohammad Ehteram, S. I. Abba, R. A. Abdulkadir, Ali Najah Ahmed, Ahmed El-Shafie. A new soft computing model for daily streamflow forecasting. Stochastic Environmental Research and Risk Assessment. 2021; ():1-13.

Chicago/Turabian Style

Saad Sh. Sammen; Mohammad Ehteram; S. I. Abba; R. A. Abdulkadir; Ali Najah Ahmed; Ahmed El-Shafie. 2021. "A new soft computing model for daily streamflow forecasting." Stochastic Environmental Research and Risk Assessment , no. : 1-13.

Journal article
Published: 20 April 2021 in IEEE Access
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Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980–2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.

ACS Style

Ali Mokhtar; Mohammadnabi Jalali; Hongming He; Nadhir Al-Ansari; Ahmed Elbeltagi; Karam Alsafadi; Hazem Ghassan Abdo; Saad Sh. Sammen; Yeboah Gyasi-Agyei; Jesus Rodrigo-Comino. Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms. IEEE Access 2021, 9, 65503 -65523.

AMA Style

Ali Mokhtar, Mohammadnabi Jalali, Hongming He, Nadhir Al-Ansari, Ahmed Elbeltagi, Karam Alsafadi, Hazem Ghassan Abdo, Saad Sh. Sammen, Yeboah Gyasi-Agyei, Jesus Rodrigo-Comino. Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms. IEEE Access. 2021; 9 ():65503-65523.

Chicago/Turabian Style

Ali Mokhtar; Mohammadnabi Jalali; Hongming He; Nadhir Al-Ansari; Ahmed Elbeltagi; Karam Alsafadi; Hazem Ghassan Abdo; Saad Sh. Sammen; Yeboah Gyasi-Agyei; Jesus Rodrigo-Comino. 2021. "Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms." IEEE Access 9, no. : 65503-65523.

Research article
Published: 24 February 2021 in Environmental Science and Pollution Research
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Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.

ACS Style

Quoc Bao Pham; Saad Sh. Sammen; Sani Isa Abba; Babak Mohammadi; Shamsuddin Shahid; Rabiu Aliyu Abdulkadir. A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation. Environmental Science and Pollution Research 2021, 28, 32564 -32579.

AMA Style

Quoc Bao Pham, Saad Sh. Sammen, Sani Isa Abba, Babak Mohammadi, Shamsuddin Shahid, Rabiu Aliyu Abdulkadir. A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation. Environmental Science and Pollution Research. 2021; 28 (25):32564-32579.

Chicago/Turabian Style

Quoc Bao Pham; Saad Sh. Sammen; Sani Isa Abba; Babak Mohammadi; Shamsuddin Shahid; Rabiu Aliyu Abdulkadir. 2021. "A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation." Environmental Science and Pollution Research 28, no. 25: 32564-32579.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ.

ACS Style

Anurag Malik; Yazid Tikhamarine; Nadhir Al-Ansari; Shamsuddin Shahid; Harkanwaljot Singh Sekhon; Raj Kumar Pal; Priya Rai; Kusum Pandey; Padam Singh; Ahmed Elbeltagi; Saad Shauket Sammen. Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1075 -1094.

AMA Style

Anurag Malik, Yazid Tikhamarine, Nadhir Al-Ansari, Shamsuddin Shahid, Harkanwaljot Singh Sekhon, Raj Kumar Pal, Priya Rai, Kusum Pandey, Padam Singh, Ahmed Elbeltagi, Saad Shauket Sammen. Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1075-1094.

Chicago/Turabian Style

Anurag Malik; Yazid Tikhamarine; Nadhir Al-Ansari; Shamsuddin Shahid; Harkanwaljot Singh Sekhon; Raj Kumar Pal; Priya Rai; Kusum Pandey; Padam Singh; Ahmed Elbeltagi; Saad Shauket Sammen. 2021. "Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1075-1094.

Original paper
Published: 19 August 2020 in Natural Hazards
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The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP), radial basis function neural network (RBFNN), and support vector machine (SVM) models to forecast meteorological droughts in Iran. The spatial–temporal pattern of droughts in Iran was also found using recorded observation data from 1980 to 2014. A nomadic people algorithm (NPA) was utilized to train the ANFIS, MLP, RBFNN, and SVM models. Additionally, the NPA was benchmarked against the bat algorithm, salp swarm algorithm, and krill algorithm (KA). The hybrid ANFIS, MLP, RBFNN, and SVM models were used to forecast the 3-month standardized precipitation index. New evolutionary algorithms were utilized to improve the convergence speed of the soft computing models and their accuracy. First, random stations, namely, in Azarbayjan (northwest Iran), Khouzestan (southwest Iran), Khorasan (northeast Iran), and Sistan and Balouchestan (southeast Iran) were selected for the testing of the models. According to the results obtained from the Azarbayjan station, the Nash–Sutcliffe efficiency (NSE) was 0.93, 0.86, 0.85, and 0.83 for the ANFIS–NPA, MLP–NPA, RBFNN–NPA, and SVM–NPA models, respectively. For Sistan and Baloucehstan, the results indicated the superiority of the ANFIS–NPA model, followed by the MLP–NPA model, compared to the RBFNN–NPA and SVM–NPA models, and suggested that the hybrid models performed better than the standalone MLP, RBFNN, ANFIS, and SVM models. The second aim of the study was to capture the relationship between large-scale climate signals and drought indices by using a wavelet coherence analysis. The general results indicated that the NPA and wavelet coherence analysis are useful tools for modelling drought indices.

ACS Style

Sedigheh Mohamadi; Saad Sh. Sammen; Fatemeh Panahi; Mohammad Ehteram; Ozgur Kisi; Amir Mosavi; Ali Najah Ahmed; Ahmed El-Shafie; Nadhir Al-Ansari. Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm. Natural Hazards 2020, 104, 537 -579.

AMA Style

Sedigheh Mohamadi, Saad Sh. Sammen, Fatemeh Panahi, Mohammad Ehteram, Ozgur Kisi, Amir Mosavi, Ali Najah Ahmed, Ahmed El-Shafie, Nadhir Al-Ansari. Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm. Natural Hazards. 2020; 104 (1):537-579.

Chicago/Turabian Style

Sedigheh Mohamadi; Saad Sh. Sammen; Fatemeh Panahi; Mohammad Ehteram; Ozgur Kisi; Amir Mosavi; Ali Najah Ahmed; Ahmed El-Shafie; Nadhir Al-Ansari. 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm." Natural Hazards 104, no. 1: 537-579.

Journal article
Published: 27 July 2020 in Applied Sciences
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A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

ACS Style

Saad Sammen; Mohammad Ghorbani; Anurag Malik; Yazid Tikhamarine; Mohammad AmirRahmani; Nadhir Al-Ansari; Kwok-Wing Chau. Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway. Applied Sciences 2020, 10, 5160 .

AMA Style

Saad Sammen, Mohammad Ghorbani, Anurag Malik, Yazid Tikhamarine, Mohammad AmirRahmani, Nadhir Al-Ansari, Kwok-Wing Chau. Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway. Applied Sciences. 2020; 10 (15):5160.

Chicago/Turabian Style

Saad Sammen; Mohammad Ghorbani; Anurag Malik; Yazid Tikhamarine; Mohammad AmirRahmani; Nadhir Al-Ansari; Kwok-Wing Chau. 2020. "Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway." Applied Sciences 10, no. 15: 5160.

Journal article
Published: 05 June 2020 in Journal of Hydrology
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Rainfall and runoff are considered the main components in the hydrological cycle. Developing an accurate model to capture the dynamic connection between rainfall and runoff remains a problematic task for engineers. Several studies have been carried out to develop models to accurately predict the changes in runoff from rainfall. However, these models have limitations in terms of accuracy and complexity when large numbers of parameters are needed. Therefore, recently, with the advancement of data-driven techniques, a vast number of hydrologists have adopted models to predict changes in runoff. However, data-driven models still encounter several limitations related to hyperparameter optimization and overfitting. Hence, there is a need to improve these models to overcome these limitations. In this study, data-driven techniques such as a Multi-Layer Perceptron (MLP) neural network and Least Squares Support Vector Machine (LSSVM) are integrated with an advanced nature-inspired optimizer, namely, Harris Hawks Optimization (HHO) to model the rainfall-runoff relationship. Five different scenarios will be examined based on the autocorrelation function (ACF), cross-correlation function (CCF) and partial autocorrelation function (PACF). Finally, for comprehensive analysis, the performance of the proposed model will then be compared with integrated data-driven techniques with particle swarm optimization (PSO). The results revealed that all the augmented models with HHO outperformed other integrated models with PSO in predicting the changes in runoff. In addition, a high level of accuracy in predicting runoff values was achieved when HHO was integrated with LSSVM.

ACS Style

Yazid Tikhamarine; Doudja Souag-Gamane; Ali Najah Ahmed; Saad Sh. Sammen; Ozgur Kisi; Yuk Feng Huang; Ahmed El-Shafie. Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization. Journal of Hydrology 2020, 589, 125133 .

AMA Style

Yazid Tikhamarine, Doudja Souag-Gamane, Ali Najah Ahmed, Saad Sh. Sammen, Ozgur Kisi, Yuk Feng Huang, Ahmed El-Shafie. Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization. Journal of Hydrology. 2020; 589 ():125133.

Chicago/Turabian Style

Yazid Tikhamarine; Doudja Souag-Gamane; Ali Najah Ahmed; Saad Sh. Sammen; Ozgur Kisi; Yuk Feng Huang; Ahmed El-Shafie. 2020. "Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization." Journal of Hydrology 589, no. : 125133.

Journal article
Published: 28 May 2020 in Applied Sciences
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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.

ACS Style

Ahmed Mohammed Sami Al-Janabi; Abdul Halim Ghazali; Badronnisa Yusuf; Saad Sh. Sammen; Haitham Abdulmohsin Afan; Nadhir Al-Ansari; Shamsuddin Shahid; Zaher Mundher Yaseen. Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity. Applied Sciences 2020, 10, 3725 .

AMA Style

Ahmed Mohammed Sami Al-Janabi, Abdul Halim Ghazali, Badronnisa Yusuf, Saad Sh. Sammen, Haitham Abdulmohsin Afan, Nadhir Al-Ansari, Shamsuddin Shahid, Zaher Mundher Yaseen. Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity. Applied Sciences. 2020; 10 (11):3725.

Chicago/Turabian Style

Ahmed Mohammed Sami Al-Janabi; Abdul Halim Ghazali; Badronnisa Yusuf; Saad Sh. Sammen; Haitham Abdulmohsin Afan; Nadhir Al-Ansari; Shamsuddin Shahid; Zaher Mundher Yaseen. 2020. "Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity." Applied Sciences 10, no. 11: 3725.

Review article
Published: 01 February 2017 in Natural Hazards
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The study of dam-break analysis is considered important to predict the peak discharge during dam failure. This is essential to assess economic, social and environmental impacts downstream and to prepare the emergency response plan. Dam breach parameters such as breach width, breach height and breach formation time are the key variables to estimate the peak discharge during dam break. This study presents the evaluation of existing methods for estimation of dam breach parameters. Since all of these methods adopt regression analysis, uncertainty analysis of these methods becomes necessary to assess their performance. Uncertainty was performed using the data of more than 140 case studies of past recorded failures of dams, collected from different sources in the literature. The accuracy of the existing methods was tested, and the values of mean absolute relative error were found to be ranging from 0.39 to 1.05 for dam breach width estimation and from 0.6 to 0.8 for dam failure time estimation. In this study, artificial neural network (ANN) was recommended as an alternate method for estimation of dam breach parameters. The ANN method is proposed due to its accurate prediction when it was applied to similar other cases in water resources.

ACS Style

Saad Sh. Sammen; T. A. Mohamed; A. H. Ghazali; L. M. Sidek; A. El-Shafie. An evaluation of existent methods for estimation of embankment dam breach parameters. Natural Hazards 2017, 87, 545 -566.

AMA Style

Saad Sh. Sammen, T. A. Mohamed, A. H. Ghazali, L. M. Sidek, A. El-Shafie. An evaluation of existent methods for estimation of embankment dam breach parameters. Natural Hazards. 2017; 87 (1):545-566.

Chicago/Turabian Style

Saad Sh. Sammen; T. A. Mohamed; A. H. Ghazali; L. M. Sidek; A. El-Shafie. 2017. "An evaluation of existent methods for estimation of embankment dam breach parameters." Natural Hazards 87, no. 1: 545-566.

Article
Published: 26 November 2016 in Water Resources Management
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Several techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash – Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods.

ACS Style

Saad Sh. Sammen; T. A. Mohamed; A. H. Ghazali; A. H. El-Shafie; L. M. Sidek. Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach. Water Resources Management 2016, 31, 549 -562.

AMA Style

Saad Sh. Sammen, T. A. Mohamed, A. H. Ghazali, A. H. El-Shafie, L. M. Sidek. Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach. Water Resources Management. 2016; 31 (1):549-562.

Chicago/Turabian Style

Saad Sh. Sammen; T. A. Mohamed; A. H. Ghazali; A. H. El-Shafie; L. M. Sidek. 2016. "Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach." Water Resources Management 31, no. 1: 549-562.