Dayo Oyeyemi is one of the leading Nigeria’s data scientists, machine learning engineers making positive impact through technology and advancing the emerging digital industry in Nigeria. He’s currently pursuing a postgraduate MSc degree in Artificial Intelligence and Data Science at the University of Hull’s Faculty of Engineering, England, United Kingdom. In this interview with Olusola Richards, Oyeyemi speaks about his experience and interests in using tech to solve problems

Can you tell us more about how your experiences before 2020 influenced your career shift to the digital technology sector?
My journey began at Obafemi Awolowo University, where I studied Animal Sciences. After graduation, I worked as a teaching assistant at Uhi Grammar School in Edo State and later as a farm manager at Seven Global Farms in Osun State. During my time as a farm manager, I experienced a devastating wildfire that destroyed wildlife, livestock, and properties. This event had a profound impact on me and sparked my interest in finding technological solutions to prevent such disasters. I decided to learn Python on YouTube tutorials, Udemy courses, StackOverflow, and Kaggle, and build my skill set in data science, software development and machine learning, with the hope of developing innovative solutions to predict and manage wildfire occurrences.

How did your career at Integrated Software Developers Group Nigeria shape your expertise in Python and AI?
Starting as a Python developer, I worked on various projects that required robust data analysis and machine learning capabilities. This experience provided a solid foundation in Python, particularly in leveraging its libraries such as NumPy, Pandas, and TensorFlow for predictive modelling and AI solutions. It was a great environment to innovate and apply these technologies to real-world problems.

How is Python being utilised in predictive modelling, and why is it such a popular choice among data scientists?
Python has become the go-to language for predictive modelling due to its simplicity, versatility, and the extensive range of libraries it offers. Libraries such as Scikit-Learn and TensorFlow provide robust tools for data manipulation, analysis, and machine learning. Python’s readability and ease of learning also make it accessible to both beginners and experienced programmers. In predictive modelling, Python allows us to build, test, and deploy models efficiently, enabling data scientists to predict future trends and behaviours based on historical data.

Can you give us an example of how Python is used in a real-world predictive modelling scenario?
One prominent example is in the financial sector, where Python is used to predict stock prices. By analysing historical stock data, economic indicators, and market sentiment, predictive models can forecast future stock movements. Another example is in healthcare, where predictive modelling helps in diagnosing diseases and predicting patient outcomes based on medical history and treatment patterns.

Shifting gears to a more pressing issue, how is AI being leveraged to combat wildfires, particularly in Nigeria?
Wildfires are a significant concern in Nigeria, especially in regions with dry climates and dense vegetation. AI can play a crucial role in mitigating wildfire risks through early detection and predictive analysis. By using satellite imagery, weather data, and vegetation maps, AI models can identify areas at high risk of wildfires. Machine learning algorithms can analyse patterns and predict the likelihood of fire outbreaks, allowing authorities to take preventive measures.

Wildfires have a devastating impact on the climate and wildlife. Can you elaborate on these impacts and why we need innovative solutions?
Absolutely. Wildfires cause significant damage to the climate by releasing large amounts of carbon dioxide and other greenhouse gases into the atmosphere, contributing to global warming. They also destroy forests, grasslands, and other natural habitats, leading to a loss of biodiversity as plants and animals perish in the flames or lose their homes. Soil erosion, water contamination, and air pollution are other severe consequences. Wildlife, particularly endangered species, are at immense risk. Their populations can be decimated, leading to imbalances in the ecosystem. Innovative solutions are needed to protect these vital natural resources and maintain ecological balance.

What specific AI technologies are you implementing to address wildfire problems in Nigeria, and how do they help preserve the ecosystem?
I am working on building a platform as a web application for real-time forest fire detection. This platform utilises a combination of remote sensing technology, drone surveillance, and machine learning algorithms. Remote sensing provides real-time data on vegetation health, moisture levels, and temperature variations. Drones equipped with infrared cameras can detect heat signatures indicative of early-stage fires. Machine learning models then process this data to predict potential fire outbreaks and their likely paths. This information is invaluable for deploying firefighting resources and implementing evacuation plans, thereby minimising climate damage and protecting wildlife habitats.

 How effective have these AI-driven solutions been in preventing and managing wildfires?
The results have been promising. In regions where AI-driven solutions have been implemented, we have seen a significant reduction in the number of uncontrolled wildfires. Early detection allows for quicker response times, and predictive modelling helps in resource allocation, ensuring that firefighting efforts are both efficient and effective. Additionally, the use of AI in public awareness campaigns has educated communities about fire prevention measures, further reducing the incidence of wildfires.

What are the challenges faced in deploying AI solutions for wildfire management in Nigeria, and how can they be overcome?
One of the primary challenges is the lack of infrastructure and technical expertise. Many regions prone to wildfires are remote and lack access to advanced technology. To overcome this, we need to invest in infrastructure development and training programs for local communities and authorities. Another challenge is the need for comprehensive data collection. Accurate predictive modelling relies on high-quality, real-time data, which can be difficult to obtain in some areas. Collaborative efforts between government agencies, research institutions, and private companies can help bridge these gaps.

Finally, looking ahead, what advancements in AI do you foresee that could further enhance wildfire management and ecosystem preservation in Nigeria?
Future advancements in AI could include the development of more sophisticated machine learning algorithms capable of better understanding and predicting fire behaviour. Integration of AI with Internet of Things (IoT) devices could provide more granular real-time data. Additionally, advancements in drone technology and satellite imagery could improve monitoring capabilities. As AI continues to evolve, its potential to prevent and manage wildfires will only grow, making it an indispensable tool in safeguarding Nigeria’s climate and wildlife.