PORT HARCOURT, NIGERIA - Nigeria's climate is mostly responsible for natural disasters and, as a result, the country has recorded more than its fair share of painful events related to such, including flooding, soil erosion, landslides, tidal waves, sand storms, oil spills, and severe levels of coastal erosion.
Although people from every region of the country sometimes partake of these tragic events, Nigeria's coastal cities face several additional difficulties and can narrate many tragic tales that are caused by the increasing tenacity of climate change. Recent floods along Nigeria's coast have proven the most damaging in decades. According to studies, residents of the country's coastal cities have indisputably endured huge losses as a result of ocean activities, including lives lost, dwellings and properties destroyed, and sources of livelihood such as farmland and businesses wiped out.
While entirely avoiding or preventing these events may be impossible, being prepared is not. The forecast of natural disasters like floods can be critical for disaster preparedness, risk reduction, and emergency response planning. This can be accomplished by studying information assets through analytics, the incorporation of the Internet of Things and artificial intelligence (AI), uncertainty analysis, and weather forecasts. Of these, the most recent and increasingly widely used innovation - known as digital twin ocean AI technology - is the most promising in efficiently delivering the greatest benefit at the lowest cost.
This article will focus on causes of flooding along coastal settlements and how the adoption and deployment of AI digital twin technology will give weather scientists an advantage in the compilation of data and forecast of potential flooding disasters in Nigeria - with relevant applications elsewhere, too, and will also cover the best manner of handling these data to reduce the likelihood of severe damage from flooding aThe content herein is subject to copyright by The Yuan. All rights reserved. The content of the services is owned or licensed to The Yuan. Such content from The Yuan may be shared and reprinted but must clearly identify The Yuan as its original source. Content from a third-party copyright holder identified in the copyright notice contained in such third party’s content appearing in The Yuan must likewise be clearly labeled as such.