The use of large-scale climate indices in monthly reservoir inflow forecasting and its application on time series and artificial intelligence models

Taereem Kim, Ju Young Shin, Hanbeen Kim, Sunghun Kim, Jun Haeng Heo

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.

Original languageEnglish
Article number374
JournalWater (Switzerland)
Volume11
Issue number2
DOIs
Publication statusPublished - 2019 Feb 1

Fingerprint

artificial intelligence
Artificial Intelligence
Climate
Artificial intelligence
time series
Time series
time series analysis
inflow
climate
neural network
artificial neural network
neural networks
Fuzzy inference
Neural networks
weather forecasting
hydrologic factors
dams (hydrology)
Adaptive systems
Weather
index

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

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abstract = "Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.",
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The use of large-scale climate indices in monthly reservoir inflow forecasting and its application on time series and artificial intelligence models. / Kim, Taereem; Shin, Ju Young; Kim, Hanbeen; Kim, Sunghun; Heo, Jun Haeng.

In: Water (Switzerland), Vol. 11, No. 2, 374, 01.02.2019.

Research output: Contribution to journalArticle

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