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)

Abstract

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.

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

“…Hybrid models have some advantages over single models, namely their ability to predict measurement variables based on ANN ensemble models: (i). increase the forecasting accuracy and are more capable of capturing the monthly inflow prediction [110], (ii). decrease the uncertainty of long-term streamflow predictions [109], (iii).…”

Dam System and Reservoir Operational Safety: A Meta-Research

“…Thus, hybrid forecasting models can improve forecasting accuracy and subsequently develop efficient reservoir operation rules. For more details about the application of hybrid models in inflow forecasting, see the comparative study performed by [209] and the review conducted by [210].…”

GeoAI in the US Geological Survey for topographic mapping

“…The application of AI to hydrology has largely focused on atmospheric modeling and image segmentation (Ball, Anderson, & Chan, 2017). However, there is a growing body of research using AI for modeling hydrologic variables (Nourani, Baghanam, Adamowski, & Kisi, 2014) such as flow (Alquraish, Abuhasel, Alqahtani, & Khadr, 2021), runoff (Han, Choi, Jung, & Kim, 2021;Kratzert et al, 2019), sediment, and flooding (Dazzi, Vacondio, & Mignosa, 2021;Han et al, 2021;Jiang, Xie, & Sainju, 2019;Schmidt, Heße, Attinger, & Kumar, 2020). Surface water modeling is informed by atmospheric modeling, yet hydrologic feature extraction often employs traditional image segmentation methods using reflectance and elevation data.…”