Next Article in Journal
Projection of Future Drought Characteristics under Multiple Drought Indices
Next Article in Special Issue
Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy)
Previous Article in Journal
A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs
Previous Article in Special Issue
Scenario-Based Real-Time Flood Prediction with Logistic Regression
 
 
Article

A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

1
Department of Mechanical Engineering, College of Engineering, University of Bisha, P.O. Box 001, Bisha 67714, Saudi Arabia
2
Department of Computer Science, College of Computing and Information Technology, University of Bisha, P.O. Box 001, Bisha 67714, Saudi Arabia
3
Department of Civil Engineering, College of Engineering, University of Bisha, P.O. Box 001, Bisha 67714, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Astray
Water 2021, 13(9), 1236; https://doi.org/10.3390/w13091236
Received: 23 March 2021 / Revised: 23 April 2021 / Accepted: 27 April 2021 / Published: 29 April 2021
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology)
The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R2 = 0.924, 0.857) and testing (R2 = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region. View Full-Text
Keywords: reservoir inflow forecasting; artificial neural fuzzy inference system; support vector machine; genetic algorithm reservoir inflow forecasting; artificial neural fuzzy inference system; support vector machine; genetic algorithm
Show Figures

Figure 1

MDPI and ACS Style

Alquraish, M.M.; Abuhasel, K.A.; Alqahtani, A.S.; Khadr, M. A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia). Water 2021, 13, 1236. https://doi.org/10.3390/w13091236

AMA Style

Alquraish MM, Abuhasel KA, Alqahtani AS, Khadr M. A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia). Water. 2021; 13(9):1236. https://doi.org/10.3390/w13091236

Chicago/Turabian Style

Alquraish, Mohammed M., Khaled A. Abuhasel, Abdulrahman S. Alqahtani, and Mosaad Khadr. 2021. "A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)" Water 13, no. 9: 1236. https://doi.org/10.3390/w13091236

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop