scholarly journals AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

2021 ◽  
Vol 25 (11) ◽  
pp. 5951-5979
Author(s):  
Yuxue Guo ◽  
Xinting Yu ◽  
Yue-Ping Xu ◽  
Hao Chen ◽  
Haiting Gu ◽  
...  
Keyword(s):  
Reservoir Operation ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Forecast Performance ◽  
Operation Optimization ◽  
Multi Objective ◽  
Streamflow Forecasts ◽  
And Performance ◽  
Forecast Quality

Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

scholarly journals AI-based techniques for multi-step streamflow forecasts: Application for multi-objective reservoir operation optimization and performance assessment

2020 ◽  
Author(s):  
Yuxue Guo ◽  
Yue-Ping Xu ◽  
Xinting Yu ◽  
Hao Chen ◽  
Haiting Gu ◽  
...  
Keyword(s):  
Reservoir Operation ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Forecast Performance ◽  
Operation Optimization ◽  
Multi Objective ◽  
Streamflow Forecasts ◽  
And Performance ◽  
Forecast Quality

Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an Artificial Intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operations performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were inputs into three AI-based models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Least Squares Support Vector Machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian Model Averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized Multi-Objective Robust Decision Making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: 1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; 2) Higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; 3) The relationship between forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?

2020 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Thomas Bosshard
Keyword(s):  
A Priori ◽  
Hydrological Regime ◽  
Climatic Conditions ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Key Drivers ◽  
Seasonal Streamflow ◽  
Forecast Quality

<p>Streamflow information for the months ahead is of great value to existing decision-making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate-related risks. Despite the large effort, there is still limited knowledge of the key drivers controlling the quality of the seasonal streamflow forecasts. In this investigation, we show that the seasonal streamflow predictability can be clustered, and hence regionalised, based on a priori knowledge of local hydro-climatic conditions. To reach these conclusions we analyse the seasonal forecasts of streamflow volumes across about 35400 basins in Europe, which vary in terms of climatology, scale and hydrological regime. We then link the forecast quality to various descriptors including physiography, hydro-climatic characteristics and meteorological biases. This allows the identification of the key drivers along a strong hydro-climatic gradient. Results show that, as expected, the seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. In addition, the predictability deteriorates with increasing lead months particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of drivers, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin’s hydrological regime, with quickly reacting basins (of low river memory) showing limited predictability. On the contrary, snow and/or baseflow dominated regions with long recessions (and hence high river memory) show high streamflow predictability. Finally, climatology and precipitation biases are also strongly related to streamflow predictability, highlighting the importance of developing robust bias-adjustment methods.</p>

scholarly journals Ensemble streamflow forecasting based on variational mode decomposition and long short term memory

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaomei Sun ◽  
Haiou Zhang ◽  
Jian Wang ◽  
Chendi Shi ◽  
Dongwen Hua ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Historical Changes ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Long Short Term Memory

AbstractReliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE = 36.3692), determination coefficient (R2 = 0.9890), mean absolute error (MAE = 9.5246) and peak percentage threshold statistics (PPTS(5) = 0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

scholarly journals A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy

2019 ◽  
Vol 22 (2) ◽  
pp. 310-326 ◽  
Author(s):  
Yujie Li ◽  
Zhongmin Liang ◽  
Yiming Hu ◽  
Binquan Li ◽  
Bin Xu ◽  
...  
Keyword(s):  
Integration Method ◽  
Model Integration ◽  
Support Vector ◽  
Climate Indices ◽  
Streamflow Forecasting ◽  
Forecast Performance ◽  
Extreme Gradient Boosting ◽  
Monthly Streamflow ◽  
Ensemble Strategy ◽  
Monthly Streamflow Forecasting

Abstract In this study, we evaluate elastic net regression (ENR), support vector regression (SVR), random forest (RF) and eXtreme Gradient Boosting (XGB) models and propose a modified multi-model integration method named a modified stacking ensemble strategy (MSES) for monthly streamflow forecasting. We apply the above methods to the Three Gorges Reservoir in the Yangtze River Basin, and the results show the following: (1) RF and XGB present better and more stable forecast performance than ENR and SVR. It can be concluded that the machine learning-based models have the potential for monthly streamflow forecasting. (2) The MSES can effectively reconstruct the original training data in the first layer and optimize the XGB model in the second layer, improving the forecast performance. We believe that the MSES is a computing framework worthy of development, with simple mathematical structure and low computational cost. (3) The forecast performance mainly depends on the size and distribution characteristics of the monthly streamflow sequence, which is still difficult to predict using only climate indices.

scholarly journals Short-Term Electric Demand Forecasting for the Residential Sector: Lessons Learned from the RESPOND H2020 Project

Proceedings ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 24
Author(s):  
Meritxell Gómez-Omella ◽  
Iker Esnaola-Gonzalez ◽  
Susana Ferreiro
Keyword(s):  
Demand Response ◽  
Demand Forecasting ◽  
Lessons Learned ◽  
Management Systems ◽  
Support Vector ◽  
Short Term ◽  
K Nearest Neighbors ◽  
Forecast Performance ◽  
Residential Sector ◽  
Energy Management Systems

RESPOND proposes an Artificial Intelligent (AI) system to assist residential consumers that would like to make use of Demand Response (DR) and incorporate it into their energy management systems. The proposed system considers the forecast energy consumption based on the data acquired. This work compares the results obtained by different forecasting methods using the Root Mean Square Error (RMSE) as a measure of the forecast performance. The ARIMA, Linear Regression (LR), Support Vector Regression (SVR) and k-Nearest Neighbors (KNN) models are tested, and it is concluded that the results achieved with the KNN obtain a better fit. In addition to obtaining the lowest RMSE, KNN is the algorithm that spends less time in obtaining the forecasts.

scholarly journals Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting

2021 ◽  
Author(s):  
Yani Lian ◽  
Jungang Luo ◽  
Jingmin Wang ◽  
Ganggang Zuo
Keyword(s):  
Climate Change ◽  
Short Term Memory ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Daily Streamflow ◽  
Runoff Forecasting ◽  
Long Short Term Memory

Abstract Many previous studies have developed decomposition and ensemble models to improve runoff forecasting performance. However, these decomposition-based models usually introduce large decomposition errors into the modeling process. Since the variation in runoff time series is greatly driven by climate change, many previous studies considering climate change focused on only rainfall-runoff modeling, with few meteorological factors as input. Therefore, a climate-driven streamflow forecasting (CDSF) framework was proposed to improve the runoff forecasting accuracy. This framework is realized using principal component analysis (PCA), long short-term memory (LSTM) and Bayesian optimization (BO) referred to as PCA-LSTM-BO. To validate the effectiveness and superiority of the PCA-LSTM-BO method with which one autoregressive LSTM model and two other CDSF models based on PCA, BO, and either support vector regression (SVR) or, gradient boosting regression trees (GBRT), namely, PCA-SVR-BO and PCA-GBRT-BO, respectively, were compared. A generalization performance index based on the Nash-Sutcliffe efficiency (NSE), called the GI(NSE) value, is proposed to evaluate the generalizability of the model. The results show that (1) the proposed model is significantly better than the other benchmark models in terms of the mean square error (MSE<=185.782), NSE>=0.819, and GI(NSE) <=0.223 for all the forecasting scenarios; (2) the PCA in the CDSF framework can improve the forecasting capacity and generalizability; (3) the CDSF framework is superior to the autoregressive LSTM models for all the forecasting scenarios; and (4) the GI(NSE) value is demonstrated to be effective in selecting the optimal model with a better generalizability.

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