Dynamic long-term streamflow probabilistic forecasting model for a multisite system considering real-time forecast updating through spatio-temporal dependent error correction

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

“…According to Koo et al [20], a universal method by which water demand can be predicted has not yet been discovered, as this depends on many factors that can vary. For example, the forecast can be made in the short term (hours, days, or weeks) [21], medium term (between one and two years) [22], or long term (more than two years) [23]. Making a short-term forecast can be essential in the decision-making process of water suppliers, organizations, or companies at the managerial level.…”

Scenario‐Based Multiobjective Robust Optimization and Decision‐Making Framework for Optimal Operation of a Cascade Hydropower System Under Multiple Uncertainties

“…Forecast errors are widely recognized as the primary factors affecting the reliability of hydropower operation strategies (Xu, Liu, et al., 2021), especially in the long‐term planning horizon, because forecast models have inherent uncertainties attributable to their input, structure, and parameters in addition to errors associated with human‐induced impression or fuzziness (Li et al., 2011; Mo et al., 2021). Under the perturbation of error sources, real‐time hydropower operation could fail to deliver water and energy to their planned levels or tolerance ranges, or reservoir storage might violate the safety operation zone, such that risk occurs.…”

“…One of the primary challenges in modeling multiple uncertainties is developing a joint distribution of dependent multivariate random variables that accurately capture the general characteristics of different types of uncertainty and dependency. The challenges can be tackled by introducing the use of copula functions (Alizadeh et al., 2018; Mo et al., 2021; Zhang et al., 2021) in recent, which builds the linkage through marginal probability distribution of random variables to their joint probability distribution. Owing to the flexibility of copula functions in formulating varied dependencies, complex correlations of varied types of forecast errors caused by cascaded influence of coupled model simulation can be captured (Chen et al., 2019; Hao & Singh, 2013).…”

“…To enhance comprehensive benefit through delivery of water and energy to the multiobjective demand side, joint operation of reservoir systems with capability to coordinate spatiotemporal water release and water head is essential to resolve conflicts regarding the allocation of water and energy resources to competing demands. In practical application of realtime hydropower operation based on forecasts, one of the most critical challenges in informing multiobjective and joint reservoir operation is how best to make efficient and robust decisions under the condition of uncertainty owing to incomplete knowledge and inaccurate forecasts, such that negative consequences of imbalanced demand and supply are manageable and expected returns or costs are acceptable (Roach et al, 2016).Forecast errors are widely recognized as the primary factors affecting the reliability of hydropower operation strategies (Xu, Liu, et al, 2021), especially in the long-term planning horizon, because forecast models have inherent uncertainties attributable to their input, structure, and parameters in addition to errors associated with human-induced impression or fuzziness (Li et al, 2011;Mo et al, 2021). Under the perturbation of error sources,…”

Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition

“…Accordingly, developing a precise and reliable model for streamflow forecasting is of high significance [3]. To date, a large number of data-driven models have been developed for streamflow forecasting [4][5][6]. However, improving the accuracy and reliability of these data-driven models still remains difficult, especially for streamflow with dramatic changes.…”