Analysis of Droughts and Their Intensities in California from 2000 to 2020

TL;DR

This study analyzes drought trends in California from 2000 to 2020, examining meteorological factors and developing a voting ensemble classifier to predict drought presence and intensity, aiding risk mitigation.

Contribution

It introduces a novel ensemble machine learning approach combining Random Forest models for drought prediction and analyzes meteorological indicators' significance.

Findings

Drought trends vary across California counties.

Meteorological indicators correlate with drought intensities.

The ensemble classifier effectively predicts drought presence and severity.

Abstract

Drought has been perceived as a persistent threat globally and the complex mechanism of various factors contributing to its emergence makes it more troublesome to understand. Droughts and their severity trends have been a point of concern in the USA as well, since the economic impact of droughts has been substantial, especially in parts that contribute majorly to US agriculture. California is the biggest agricultural contributor to the United States with its share amounting up to 12% approximately for all of US agricultural produce. Although, according to a 20-year average, California ranks fifth on the list of the highest average percentage of drought-hit regions. Therefore, drought analysis and drought prediction are of crucial importance for California in order to mitigate the associated risks. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index remains a challenging task. In the present study, we trained a Voting Ensemble classifier utilizing a soft voting system and three different Random Forest models, to predict the presence of drought and also its intensity. In this paper, initially, we have discussed the trends of droughts and their intensities in various California counties reviewed the correlation of meteorological indicators with drought intensities and used these meteorological indicators for drought prediction so as to evaluate their effectiveness as well as significance.