Multi-station joint forecast model of water level in Hongze lake based on PSO-LightGBM

Author(s):  
Tao Sun ◽  
Yibin Wang ◽  
Xin Jin ◽  
Zhicheng Zha
2021 ◽  
Vol 13 (9) ◽  
pp. 4857
Author(s):  
Zitong Yang ◽  
Xianfeng Huang ◽  
Jiao Liu ◽  
Guohua Fang

In order to meet the demand of emergency water supply in the northern region without affecting normal water transfer, considering the use of the existing South-to-North Water Transfer eastern route project to explore the potential of floodwater resource utilization in the flood season of Hongze Lake and Luoma Lake in Jiangsu Province, this paper carried out relevant optimal operating research. First, the hydraulic linkages between the lakes were generalized, then the water resources allocation mode and the scale of existing projects were clarified. After that, the actual available amount of flood resources in the lakes was evaluated. The average annual available floodwater resources in 2003–2017 was 1.49 billion m3, and the maximum available capacity was 30.84 billion m3. Then, using the floodwater resource utilization method of multi period flood limited water levels, the research period was divided into the main flood season (15 July to 15 August) and the later flood season (16 August to 10 September, 11 September to 30 September) by the Systematic Clustering Analysis method. After the flood control calculation, the limited water level of Hongze Lake in the later flood season can be raised from 12.5 m to 13.0 m, and the capacity of reservoir storage can increase to 696 million m3. The limited water level of Luoma Lake can be raised from 22.5 m to 23.0 m (16 August to 10 September), 23.5 m (11 September to 30 September), and the capacity of reservoir storage can increase from 150 to 300 million m3. Finally, establishing the floodwater resource optimization model of the lake group with the goals of maximizing the floodwater transfer amount and minimizing the flood control risk rate, the optimal water allocation scheme is obtained through the optimization algorithm.


2020 ◽  
Vol 46 (6) ◽  
pp. 1511-1520
Author(s):  
Yu Cai ◽  
Chang-Qing Ke ◽  
Xiaoyi Shen

2013 ◽  
Vol 304 ◽  
pp. 85-94 ◽  
Author(s):  
Yixing Yin ◽  
Ying Chen ◽  
Shutong Yu ◽  
Wucheng Xu ◽  
Wen Wang ◽  
...  

2002 ◽  
Vol 14 (1) ◽  
pp. 19-24
Author(s):  
XUE Lianqing ◽  
◽  
CUI Guangbai ◽  
CHEN Kaiqi

2021 ◽  
Author(s):  
Antonio Francipane ◽  
Elisa Arnone ◽  
Leonardo Valerio Noto

<p>Artificial reservoirs are one of the main water supply resources in the Mediterranean areas; their management can be strongly affected by the problems of drought and water scarcity. The reservoir water level is the result of the hydrological processes occurring in the upstream catchment, which, in turn, depend on meteorological variables, such as rainfall and temperature. It follows that a reliable forecast model of the meteorological forcing, along with a reliable water balance model, could enhance the correct management of a reservoir. With regard to the rainfall/temperature forecast model, the use of forecast climate data in the mid-term may provide further support for the future water level estimation of reservoirs.</p><p>From the perspective of the water balance model, instead, among the approaches used to predict the water levels for the next future, those based on data-driven methods have been demonstrated to be particularly capable of correctly reproducing the correlation between a dependent variable (e.g., water level, volume) and some covariates (e.g., temperature, precipitation).</p><p>This study describes the preliminary results of a novel application that exploits the Seasonal Forecast (SF) data, produced at the European Centre for Medium-Range Weather Forecasting (ECMWF), within a data-driven model aimed to predict the reservoir water volume at mid-term scale, up to 6 months ahead in four reservoirs of the Sicily (Italy) here considered as a case study. For each case, a NARX (Nonlinear AutoRegressive network with eXogenous inputs) neural network is calibrated to reproduce the monthly stored water volume starting from the monthly precipitation and mean monthly air temperature variables.</p><p>Preliminary results showed that the NARXs have the capability to reproduce the water levels in the investigated period (January 2017 - April 2020), including the variations during more or less dry periods. All this despite the SF data have not been previously treated with downscaling and/or bias correction techniques.</p>


Author(s):  
Wei Ming Wong ◽  
Mohamad Yusry Lee ◽  
Amierul Syazrul Azman ◽  
Lew Ai Fen Rose

The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.


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