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Title: Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Abstract

Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditionsmore » are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [2]; ORCiD logo [2]; ORCiD logo [4]
  1. Univ. of California, Irvine, CA (United States); Deltares USA Inc., Silver Springs, MD (United States)
  2. Univ. of California, Irvine, CA (United States)
  3. Deltares USA Inc., Silver Springs, MD (United States)
  4. Chinese Academy of Sciences (CAS), Beijing (China)
Publication Date:
Research Org.:
Univ. of California, Irvine, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1466038
Alternate Identifier(s):
OSTI ID: 1402404
Grant/Contract Number:  
IA0000018; 4600010378; CCF-1331915; NA09NES4400006; 2009-1380-01
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal Issue: 4; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; reservoir inflow; Trinity Lake; Danjiangkou Reservoir; artificial intelligence; data mining; climate indices

Citation Formats

Yang, Tiantian, Asanjan, Ata Akbari, Welles, Edwin, Gao, Xiaogang, Sorooshian, Soroosh, and Liu, Xiaomang. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. United States: N. p., 2017. Web. doi:10.1002/2017WR020482.
Yang, Tiantian, Asanjan, Ata Akbari, Welles, Edwin, Gao, Xiaogang, Sorooshian, Soroosh, & Liu, Xiaomang. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. United States. doi:10.1002/2017WR020482.
Yang, Tiantian, Asanjan, Ata Akbari, Welles, Edwin, Gao, Xiaogang, Sorooshian, Soroosh, and Liu, Xiaomang. Thu . "Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information". United States. doi:10.1002/2017WR020482. https://www.osti.gov/servlets/purl/1466038.
@article{osti_1466038,
title = {Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information},
author = {Yang, Tiantian and Asanjan, Ata Akbari and Welles, Edwin and Gao, Xiaogang and Sorooshian, Soroosh and Liu, Xiaomang},
abstractNote = {Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.},
doi = {10.1002/2017WR020482},
journal = {Water Resources Research},
issn = {0043-1397},
number = 4,
volume = 53,
place = {United States},
year = {2017},
month = {3}
}

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Works referenced in this record:

Induction of decision trees
journal, March 1986


Bagging predictors
journal, August 1996


Random Forests
journal, January 2001