Importance of multiple time step optimization in river basin planning and management: a case study of Damodar River basin in India

This paper puts forward and studies the concept, features as well as regulation process of real-time dispatch of basin water resources. The real-time regulation of basin water resources features temporal regulation system of multiple time-step nesting, dynamic balance regulation system of self-adapting demand-supply, dynamic information-based feedback regulation system, and three-element regulation system in combination of reservoir dispatch, river reach water distribution and flow forecast control. With example of lower Yellow River reaches below Xiaolangdi, a real-time regulation model of water resources in river basin with multiple time-step nesting and scrolling-amendment has been developed. The developed model has been put into actual practice and resulted in the safety of water supply and avoid of dry-up in consecutive low flow yeas of Yellow River Basin.

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Toxicity and health risk assessment of polycyclic aromatic hydrocarbons in surface water, sediments and groundwater vulnerability in Damodar River Basin

Groundwater for Sustainable Development ◽

10.1016/j.gsd.2021.100553 ◽

2021 ◽

Vol 13 ◽

pp. 100553

Author(s):

Balram Ambade ◽

Shrikanta Shankar Sethi ◽

Sudarshan Kurwadkar ◽

Amit Kumar ◽

Tapan Kumar Sankar

Keyword(s):

Risk Assessment ◽

Polycyclic Aromatic Hydrocarbons ◽

Surface Water ◽

Health Risk ◽

River Basin ◽

Aromatic Hydrocarbons ◽

Groundwater Vulnerability ◽

Polycyclic Aromatic ◽

Damodar River ◽

Damodar River Basin

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An automated Verlet neighbor list algorithm with a multiple time-step approach for the simulation of large systems

Computer Physics Communications ◽

10.1016/0010-4655(92)90108-b ◽

1992 ◽

Vol 70 (3) ◽

pp. 467-477 ◽

Cited By ~ 5

Author(s):

Ariel A. Chialvo ◽

Pablo G. Debenedetti

Keyword(s):

Multiple Time ◽

Time Step ◽

Multiple Time Step ◽

Neighbor List ◽

Large Systems

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Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽

10.3390/en13143517 ◽

2020 ◽

Vol 13 (14) ◽

pp. 3517 ◽

Cited By ~ 1

Author(s):

Anh Ngoc-Lan Huynh ◽

Ravinesh C. Deo ◽

Duc-Anh An-Vo ◽

Mumtaz Ali ◽

Nawin Raj ◽

...

Keyword(s):

Deep Learning ◽

Solar Radiation ◽

Short Term Memory ◽

Global Solar Radiation ◽

Multiple Time ◽

Short Term ◽

Time Step ◽

Term Memory ◽

Multiple Time Step ◽

Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

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