Comment on: “Escalante-Sandoval, C. and L. Amores-Rovelo. 2017. Regional monthly runoff forecast in Southern Canada using ANN, K-means, and L-moments techniques. Canadian water resources journal 42(3): 205-222.”

2019 ◽  
Vol 45 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Paul H. Whitfield
Keyword(s):  
Water Resources ◽  
L Moments ◽  
Runoff Forecast ◽  
Monthly Runoff

Runoff Forecast and Analysis of the Probability of Dry and Wet Transition in the Hanjiang River Basin

2021 ◽  
Author(s):  
Haoyu Jin ◽  
Xiaohong Chen ◽  
Ruida Zhong
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Transition Probability ◽  
Conversion Model ◽  
Drought And Flood ◽  
Runoff Prediction ◽  
Runoff Forecast ◽  
Hanjiang River ◽  
Basin Area ◽  
Regional Water

Abstract Runoff prediction has an important guiding role in the planning and management of regional water resources, flood prevention and drought resistance, and can effectively predict the risk of changes in regional water resources. This study used 12 runoff prediction methods to predict the runoff of four hydrological stations in the Hanjiang River Basin (HRB). Through the MCMC method, the HRB runoff probability conversion model from low to high (high to low) is constructed. The study found that the runoff of the HRB had a decreasing trend. In the mid-1980s, the runoff had a significant decreasing trend. The smoother the runoff changes, the easier it is to make accurate prediction. On the whole, the QS-MFM, MFM, MA-MFM, CES and DNN methods have strong generalization ability and can more accurately predict the runoff of the HRB. The Logistic model can accurately simulate the change of runoff status in the HRB. Among them, the HLT station has the fastest conversion rate of drought and flood, and the flow that generates floods is 6 times that of drought. The smaller the basin area, the larger the gap between drought and flood discharge. Overall, this research provides important technical support for the prediction of change in water resources and the transition probability from drought to flood in the HRB.

Long-term Runoff Forecasting Models Based on the Teleconnection coupled with Machine Learning

2020 ◽  
Author(s):  
Teng Zhang ◽  
Zhongjing Wang ◽  
Zixiong Zhang
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Model Performance ◽  
Support Vector ◽  
Wet Season ◽  
Model Based ◽  
Runoff Forecasting ◽  
Monthly Runoff ◽  
Highly Correlated ◽  
The Impact

<p>Runoff forecast with high precision is important for the efficient utilization of water resources and regional sustainable development, especially in the arid area. The monthly runoff of Changmabao (CMB) station has an upwards trend and an abrupt point in 1998. The impact factor analysis shows that it is highly correlated with the current precipitation and temperature in the wet season while the previous runoff and previous global land temperature in the dry season. Three models including the time-series decomposition model, the model based on teleconnection coupled with the support vector machine, and the model based on teleconnection coupled with the artificial neural network are used to predict the runoff of CMB station. An indicator β is constructed with the correlation coefficient (R) and mean relative deviation (rBias) to evaluate the model performance more conveniently and intuitively. The results suggest that the model based on teleconnection coupled with the support vector machine preforms best. This forecasting method could be applied to the management and dispatch of water resources in arid areas.</p>

Wavelet ANN Based Monthly Runoff Forecast

2013 ◽  
Vol 421 ◽  
pp. 803-807
Author(s):  
Hui Jun Xu
Keyword(s):  
Neural Network ◽  
Jiangxi Province ◽  
Good Precision ◽  
Hydro Power ◽  
Runoff Series ◽  
Inflow Forecasting ◽  
Runoff Forecast ◽  
Monthly Runoff ◽  
Different Time Scales

A wavelet artificial neural network to forecasting monthly runoff is proposed. The monthly runoff series is firstly decomposed to sub-series on different time scales, and each sub-series is modeled. The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Then the proposed network is trained with 49 years (1952-2000) actual data of one hydro power plant of Jiangxi province and is tested for target year (2001-2003). Finally, some actual results for mid and long term water inflow forecasting are obtained and which show the proposed method has a good precision for forecasting.

WANN Model for Monthly Runoff Forecast

2008 ◽  
Author(s):  
Huifang Guo ◽  
Zengchuan Dong ◽  
Xin Chen ◽  
Xixia Ma ◽  
Peiyan Zhang
Keyword(s):  
Runoff Forecast ◽  
Monthly Runoff

Monthly Runoff Probabilistic Forecast Model Based on Similar Process Derivations

2015 ◽  
Vol 737 ◽  
pp. 710-714
Author(s):  
Cai Lin Lee ◽  
Dong Mei Wang
Keyword(s):  
Forecast Accuracy ◽  
Forecast Model ◽  
Probabilistic Forecast ◽  
Similar Process ◽  
Simulation Experiments ◽  
Model Combining ◽  
Forecast Result ◽  
Probabilistic Forecasts ◽  
Runoff Forecast ◽  
Monthly Runoff

In this paper, a runoff forecast model combining similar process derivation with probabilistic forecasts is proposed. Certain forecast result is computed by similar processes derivations, and on the basis of certain results, a confidence interval under given confidence coefficient is worked out by probabilistic forecast part. The model is simple in structure, easy in establishing and unnecessary to concern for predictor selections. Applying above model in simulation experiments, the results show the forecast model have excellent forecast accuracy and can be used in monthly runoff forecast effectively.

scholarly journals Water resources assessment and prediction in China

2016 ◽  
Vol 374 ◽  
pp. 79-84
Author(s):  
Wang Guangsheng ◽  
Dai Ning ◽  
Yang Jianqing ◽  
Wang Jinxing
Keyword(s):  
Water Resources ◽  
Low Flow ◽  
Ensemble Prediction ◽  
Probability Method ◽  
Industrial Project ◽  
Runoff Prediction ◽  
Water Resources Assessment ◽  
Annual Water ◽  
Monthly Runoff ◽  
Resources Assessment

Abstract. Water resources assessment in China, can be classified into three groups: (i) comprehensive water resources assessment, (ii) annual water resources assessment, and (iii) industrial project water resources assessment. Comprehensive water resources assessment is the conventional assessment where the frequency distribution of water resources in basins or provincial regions are analyzed. For the annual water resources assessment, water resources of the last year in basins or provincial regions are usually assessed. For the industrial project water resources assessment, the water resources situation before the construction of industrial project has to be assessed. To address the climate and environmental changes, hydrological and statistical models are widely applied for studies on assessing water resources changes. For the water resources prediction in China usually the monthly runoff prediction is used. In most low flow seasons, the flow recession curve is commonly used as prediction method. In the humid regions, the rainfall-runoff ensemble prediction (ESP) has been widely applied for the monthly runoff prediction. The conditional probability method for the monthly runoff prediction was also applied to assess next month runoff probability under a fixed initial condition.

A quarter century of Water Resources Research

1990 ◽  
Vol 26 (1) ◽  
pp. 2-4 ◽  
Author(s):  
George H. Davis
Keyword(s):  
Water Resources ◽  
Quarter Century
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