Brendon James
Artificial intelligence (AI) subtly yet significantly influences our daily digital interactions. From the tailored news feeds on our social media platforms to the eerily accurate friend suggestions and targeted advertisements, AI's footprint is undeniable.
The advent of generative AI, exemplified by innovations such as ChatGPT, has brought the commercial potential of AI to the forefront.
Developed by OpenAI, ChatGPT is part of a broader category of technology known as large language models (LLMs), which are designed to understand, generate and interact with human language in a way that's both comprehensive and contextually relevant.
This evolution of AI from a behind-the-scenes actor to a headline-grabbing phenomenon raises critical questions about its application beyond the digital entertainment and advertising realms, particularly in addressing the pressing challenges of the real economy.
As professionals in the energy sector, we find ourselves at the forefront of combating one of the most daunting challenges of our era – climate change.
The imperative for climate adaptation has never been more urgent, with the increasing frequency of extreme weather events highlighting the vulnerability of our communities and economies.
Climate adaptation involves developing and implementing strategies to mitigate the impacts of climate change, from enhancing infrastructure resilience to adopting sustainable agricultural practices.
Challenge of flooding
TT, like many island nations, faces significant risks from flooding. This is exacerbated by its geographical location, which makes it prone to heavy rainfall events, particularly during the hurricane season. The country has experienced several devastating floods in recent years, affecting thousands of lives and causing substantial economic damage. The need for effective flood prediction and management strategies is therefore critical.
[caption id="attachment_1079391" align="alignnone" width="1024"] Figure 2: An AI 2019 data frame showing the peak months of rainfall and flooding.Graphic courtesy GSTT -[/caption]
This leads to the hypothesis: Can AI effectively predict flooding in TT?
To explore this, I embarked on a project using the current open-source machine learning (ML) and AI tools, armed with nothing but a laptop and a healthy dose of curiosity.
The journey began with the search for relevant data, a quest that led me to a treasure trove of weather data spanning over a decade.
Using Beautiful Soup, a Python library for web scraping, I compiled five years of weather data from 2019-2023, encompassing rainfall, temperature and cloud coverage.
Identifying reliable data on flooding events posed a significant challenge because of the scarcity of public records. However, by exploring resources such as the Meteorological Office, TT Weather Centre, Relief Web and the Caribbean Catastrophe Risk Insurance Facility, I managed to gather comprehensive flooding data for the same period. The data frame build identified 2,266 flooding events ove
