Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

Ehteram, Mohammad; Ahmed, Ali Najah; Latif, Sarmad Dashti; Huang, Yuk Feng; Alizamir, Meysam; Kişi, Özgür; Mert, Cihan; El-Shafie, Ahmed

doi:10.1007/s11356-020-10421-y

Cited by 58 publications

(22 citation statements)

References 37 publications

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“…Many scholars have stated their tendency regarding the use of hybrid methods for similar issues (e.g., sediment concentration [85] and salinity [86] predictions). The reason for the development of such models can be the use of an optimization technique in the position of a trainer algorithm.…”

Section: Further Discussionmentioning

confidence: 99%

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

2021

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Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.

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Section: Further Discussionmentioning

confidence: 99%

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

2021

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“…A multi-objective whale optimization algorithm proposed by Wang et al [ 33 ] is used to solve the energy-efficient distributed permutation flow shop scheduling problem. Ehteram et al [ 34 ] proposed a hybrid Artificial neural network (ANN) with a multi-objective whale optimization algorithm designed to perform suspended sediment load prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Finding a satisfying solution is apparently required. Second, the whale optimization algorithm is universal for many application areas with the advantages mentioned, and has many successful applications [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Third, WOA is used to solve the optimization strategy of computing offloading [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Huang

Zhai

Chen

et al. 2021

Sensors

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Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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“…Other studies worldwide seeking to enhance the precision of the SSL estimation have used machine learning techniques such as adaptive neuro-fuzzy system (ANFIS) (Rajaee et al 2009;Cobaner et al 2009;Kisi et al 2012;Azamathulla et al 2012;Vafakhah 2013;Choubin et al 2018), artificial neural network (ANN) (Rajaee et al 2009;Melesse et al 2011;Kisi et al 2012;Vafakhah 2013;Nourani and Andalib 2015;Wang et al 2018;Halecki et al 2018;Liu et al 2019), support vector machine (SVM) (Kisi et al 2012;Pektaş and Dogan 2015;Choubin et al 2018), multilayer perceptron (MLP) (Cigizoglu 2004;Gholami et al 2016;Romano et al 2018), and radial basis function neural network (RBFNN) (Erol et al 2008;Ahmad and Kumar 2016;Ibrahim et al 2019). The soft computing models were widely applied for predicting SSL, e.g., Adib and Mahmoodi (2017) were applied GA method to optimize the structure of the ANN model predicting SSL, Talebi et al (2017) estimated SSL using regression trees and ANN models, Salih et al (2020) have illustrated that the attribute selected classifier performed better than the tree models in SSL prediction, Ehteram et al (2020) have employed ANN and a multiobjective genetic algorithm to predict the SSL, and Samantary and Ghose (2020) estimated SSL using SVM, feed-forward neural network (FFN), and RBFNN and they have shown that the SVM had the highest performance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

et al. 2021

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Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.

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Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

Cited by 58 publications

References 37 publications

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

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