An Optimal Integration of Multiple Machine Learning Techniques to Real-Time Reservoir Inflow Forecasting

A reservoir inflow forecasting system represents a crucial technique in reservoir operation and disaster prevention, particularly in areas where the primary water source derives from typhoon events. This includes the study area of the current research, i.e., the Shimen Reservoir (Taiwan). Effectively depositing short and high-intensity rainfall and avoiding disaster losses present significant challenges in this regard. However, the high variability and uncertainty of such rainfall events make them difficult to forecast using traditional physical-based models, which require too many calculations for application in real-time disaster forecasting. Accordingly, in this study, seven machine learning (ML) algorithms, including three conventional ML and four deep learning algorithms, were compared to derive their effectiveness for reservoir inflow forecasting in extreme weather events. The forecasting lead-times were set to 1, 4, and 6-h, representing short, medium, and long-term forecasting, respectively. Moreover, to ensure the stability and credibility of the models, two types of integrated approaches, ensemble means and switched prediction method (SP) were also employed. The results showed that although an optimal algorithm could be selected for the short, medium, and long-term, individual algorithms did not always perform well in all events. Nonetheless, the integrated approaches can effectively combine the advantages of all the included algorithms and generate more accurate and stable forecasting results, particularly when using SP, which was involved in the top three performances among all typhoon examples and indicated the best average performance. Accordingly, when using a single forecasting algorithm, gated recurrent unit, a type of transformed recurrent neural network, will yield the best performance. Furthermore, integrated forecasts, particularly involving SP, can effectively improve the accuracy and stability of forecasts to render a model more applicable to an actual situation.

Mid-Long Term Inflow Forecasting Using the J-T Test and Long Short-Term Memory Considering Monthly factors

Mid-long term inflow forecasting plays an important supporting role in reservoir production planning, drought and flood control, comprehensive utilization and water resource management. Although the inflow data have some periodicity and predictability characteristics, the inflow sequence has complex nonlinearity due to the comprehensive influence of climate, underlying surfaces, human activities and other factors. Therefore, it is difficult to achieve accurate inflow forecasting. In this study, a new hybrid inflow forecast framework that uses previous inflows and monthly factors as inputs, and that adopts Long Short-Term Memory (LSTM) and the Jonckheere-Terpstra test (J-T test) is developed for mid-long term inflow forecasting. First, the J-T test can test whether the monthly average inflow sequence set exhibits significant differences due to climate, underlying surfaces, human activities and other factors to ensure the effectiveness of the framework. Second, the LSTM, which is good at determining the nonlinearity law of the time sequence and finding the best solution, is chosen as the framework algorithm. Finally, due to the periodicity of the inflow sequence, adding monthly factors into the framework can provide more information for the framework to improve the accuracy of the forecast. Xiaowan Hydropower Station in the Lancang River of China is selected as the research area. Six evaluation criteria are used to evaluate established framework using historical monthly inflow data (January 1954-December 2016). The performance of the framework is compared with that of the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. The results show that the introduction of monthly factors greatly improves the accuracy of the inflow forecast studied, and the proposed method is also better than other frameworks.

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)

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.

Application of Xin'anjiang Model and Wetspa Model in the Inflow Forecasting of Shiquan Reservoir

<p>In order to improve the accuracy of the inflow forecasting of Shiquan Reservoir in the Han River Basin, this paper compared the application effects of Xin'anjing model and Wetspa model. The study collected the rainfall and runoff data from 2009 to 2015, as well as the DEM, land use and soil data with 1000´1000m grid size. The model calibration and verification periods were from 2009 to 2012 and from 2013 to 2015, respectively. In addition to using the runoff depth and the determination coefficient to evaluate the accuracy of the two models, the flow relative error CR1, model confidence coefficient CR2, Nash-Sutcliffe efficiency CR3, logarithmic version of Nash-Sutcliffe efficiency CR4 for low flow, improved Nash-Sutcliffe efficiency CR5 for high flow were adopted to analyze the simulation results of the two models. The results showed that the simulation results of the Wetspa model could be used as a supplement to the simulation results of the Xin'anjiang model, providing high-precision flood forecasting results for the scheduling decisions of Shiquan Reservoir in terms of time and space.</p>