Lee, J., & Hwang, Y., & Kim, T. (2020, June), Forecasting Drought Indices Using Machine Learning Algorithm  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34680

\bibitem{asee_peer_34680}
  Lee, J., \& Hwang, Y., \& Kim, T. (2020, June), \emph{Forecasting Drought Indices Using Machine Learning Algorithm}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--34680

Jay Lee P.E., Yeonsang Hwang P.E., and Tae-Hoon Kim.  "Forecasting Drought Indices Using Machine Learning Algorithm".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/34680 Internet.  05 Jul, 2024

\bibitem{asee_peer_34680}
  Jay Lee P.E., Yeonsang Hwang P.E., and Tae-Hoon Kim.
  "Forecasting Drought Indices Using Machine Learning Algorithm".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/34680 Internet.  05 Jul, 2024

@INPROCEEDINGS{asee_peer_34680
author = "Jay Lee P.E., Yeonsang Hwang P.E., and Tae-Hoon Kim"
title = "Forecasting Drought Indices Using Machine Learning Algorithm"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/34680}
number = {10.18260/1-2--34680}
}

TY  - CPAPER
AB  - According to the existing studies, the historical climate record and seasonal temperature and precipitation records offer useful input for making short-term drought predictions. In the last few decades, numerous studies have been conducted to explore these data in a way to predict upcoming drought events. Despite the efforts, few studies have succeeded in quantifying uncertainties in the process of predicting drought index values due mainly to technical challenges and implications in computation. This paper proposes a new approach utilizing an artificial intelligence model for forecasting drought indices. This study uses a regression analysis model in machine learning, Lasso, which is normalized to improve the prediction accuracy. Lasso model will be implemented in Python using scikit-learn, and 10-fold cross-validation will be used to ensure the prediction accuracy. The proposed model uses the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) seasonal data to compute the Palmer Drought Severity Index (PDSI). The accuracy of the model is validated using the historical records of drought indices and available seasonal temperature and precipitation data provided by the NOAA CPC. The results of the forecasts produced by this model will be compared with the observed drought indices and validated. The mean error rate and root mean square error (RMSE) methods are used to measure the accuracy of the forecast at stations for validation. The validated model can be used in classroom and laboratory settings for general engineering studies.
AU  - Jay Lee P.E.
AU  - Yeonsang Hwang P.E.
AU  - Tae-Hoon Kim
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Forecasting Drought Indices Using Machine Learning Algorithm
UR  - https://peer.asee.org/34680
DO  - 10.18260/1-2--34680
ER  -