Predicting drought using Bayesian structural time series model

Authors

  • Sophia Wang Monta Vista High School
  • Connor Lee Saratoga High School
  • Dr. XL Pang Mentor, Monta Vista High School

DOI:

https://doi.org/10.47611/jsrhs.v10i3.1907

Keywords:

Drought prediction, Statistical modeling

Abstract

The western U.S. has been experiencing a mega-scale drought since 2000. By killing trees and drying out forests, the drought triggers widespread wildfire activities. In the 2020 California fire season alone, more than 10.3 million acres of land were burned and over 10000 structures were damaged. The estimated cost is over $12 billion. Drought also devastates agriculture and drains the social and emotional well-being of impacted communities. 

This work aims at predicting the occurrence and severity of drought, and thus helping mitigate drought related adversaries. A machine learning based framework was developed, including time series data collection, model training, forecast and visualization. The data source is from the National Drought Monitor center with FIPS (Federal Information Processing Standards) geographic identification codes. For model training and forecasting, a Bayesian structural time series (BSTS) based statistical model was employed for a time-series forecasting of drought spatially and temporally. In the model, a time-series component captures the general trend and seasonal patterns in the data; a regression component captures the impact of the drought in measurements such as severity of drought, temperature, etc. The statistical measure, Mean Absolute Percentage Error, was used as the model accuracy metric. The last 10 years of drought data up to 2020-09-01 was used for model training and validation. Back-testing was implemented to validate the model . Afterwards, the drought forecast was generated for the upcoming 3 weeks of the United States based on the unit of county level. 2-D heat maps were also integrated for visual reference. 

 

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References or Bibliography

U. S. Drought Monitor, West, National Drought Mitigation Center, University of Nebraska-Lincoln, www.droughtmonitor.unl.edu/CurrentMap/StateDroughtMonitor.aspx?West

Seeing Theory, Chapter 4, Frequentist Inference, Chapter 5, Bayesian Inference, Brown University, www. Seeing-theory.brown.edu

A. K. Mishra and V. R. Desai, DroDrought-forecasting-using-stochastic-models.pdf, Stoch Environ Res Risk Assess, 2005, 19: 326-339

JiYae Shin, Muham mad Ajmal, and Jiyoung Yoo, A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook and Tae-Woong Kim, Advances in Meteorology, 2016

Steven L. Scott and Hal Varian, Predicting the Present with Bayesian Structural Time Series, International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 5 , 4-23, 2014.

Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, Chapter 8, ARIMA models.

Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy and Steven L. Scott, Inferring Causal Impact Using Bayesian Structural Time-series Models, Annals of Applied Statistics, Vol 9, No 1, 247-274, 2015.

Stephen Brooks, Markov chain Monte Carlo method and its application, Journal of the Royal Statistical Society: Series D (The Statistician), Vol 47, No 1, 69-100, 2002.

Swamidass P.M. (eds), MAPE (mean absolute percentage error), 462, Encyclopedia of Production and Manufacturing Management. Springer, Boston, MA .

Published

11-06-2021

How to Cite

Wang, S., Lee, C. ., & Pang, X. (2021). Predicting drought using Bayesian structural time series model . Journal of Student Research, 10(3). https://doi.org/10.47611/jsrhs.v10i3.1907

Issue

Section

HS Research Articles