Sea Level Rise Prediction in Miami Beach using Continuous Time Markov Chains, Random Forest Regressions, and Seasonal ARIMA Models
Arihant Jaggi
NHSJS
National High School Journal of Science
is a free, online, peer-reviewed research journal for high school students, with submissions reviewed by scientists and student editors.
ABSTRACT
Rapid sea level rise poses immediate risks to coastal cities, like Miami Beach, with accurate forecasting technologies having the potential to inform infrastructure and adaptation. This study evaluates probabilistic state-transition models (Continuous-Time Markov Chains) and compares its effectiveness and accuracy to Random Forest (RF) Regressions and Seasonal ARIMA (SARIMA) models using identical datasets and … [view more]
Keywords: Miami Beach; Sea Level Rise; Continuous-Time Markov Chains; Random Forest; SARIMA; Time-Series Forecasting
Awards & Recognition
This paper was recognized by Sigma Xi, The Scientific Research Honor Society — including a global first place in Marine and Climate Science at the Student Research Showcase and a Top 300 selection at IFORE — and earned a CREST Gold Award from the British Science Association.
Sigma Xi Student Research Showcase
1st Place Worldwide — Marine & Climate Science
First place globally in Marine and Climate Science for this predictive Markov-chain sea level rise project.
Sigma Xi IFoRE
Top 300 Worldwide — International Forum on Research Excellence
Selected among the top 300 globally at IFoRE, powered by Sigma Xi, for the same Miami Beach sea level modeling research.
CREST Gold Award
British Science Association
Gold Award for predictive modeling of sea level rise in Miami Beach using Markov chains and machine learning.