Introduction

Environmental challenges are becoming increasingly complex, requiring innovative tools for understanding, managing, and mitigating human impact on the planet. The intersection of environmental science and data science, particularly the application of advanced technology such as artificial intelligence (AI), including its subset machine learning (ML), combined with remote sensing and geospatial analysis, has unlocked groundbreaking approaches to climate, deforestation, biodiversity loss, water management, and agricultural productivity. 

On November 29-11-24, Mr. Meshach O. Aderele, Data Scientist and PhD Fellow at Land-Craft, Aarhus University, Denmark explored how data-driven insights transform decision-making, promote sustainability, and drive innovation in environmental research and policy.

Key Messages

Data science is a combination of statistics, technology, and domain expertise used to analyze data and extract meaningful insights. 

Components of data science

Data collection– This is the process of gathering the right information that represents real-life scenarios of things.

Data analysis– This is the process of finding patterns and trends. It can be achieved through correlation, regression, and testing various variables against each other i.e. irrigation amount with the yield on farming to determine if higher irrigation leads to higher yields or low irrigation leads to higher yields. Correlation between tillage and yield can be carried out to show the kind of tillage practiced, the timing of the tillage and the resulting impact from the farmland, and the environmental impact. These patterns can be analyzed through data analysis by the creation of a predictive model using the data to make predictions on what the outcomes would be by taking certain actions.

Decision-making– Data science analysis is only useful when used to solve problems and help policymakers, and farmers make decisions. 

Data Science Techniques.

Artificial Intelligence– this is a set of technologies that enable computers to perform a variety of advanced functions including the ability to see, understand, and translate spoken and written language, analyse data, and make recommendations.

Machine Learning is the ability of a machine to train computer programs to recognize patterns based on algorithms.

Neural Network is a computer system designed to imitate the neurons in a human brain.

Natural Language Selection– this is the ability to understand speech and analyse documents.

Robotics- Machine that assists people without actual human involvement.

The power of data science in environmental science.

Data science enables us to analyze large datasets, create models, and find patterns that would be difficult to observe. About environmental applications, data science entails translating environmental challenges into data-driven computational problems. Researchers from NASA use machine learning to analyze satellite data and predict climate patterns and impacts on sea level rise, temperature changes, and storm frequencies. These models have been used to support climate policy and risk management efforts globally.

Applications in Environmental Science

Deforestation and monitoring- Global Forest Watch uses AI and satellite imagery to track deforestation, and identify illegal logging activities and forest degradation. These tools aid governments and NGOs in protecting critical forest areas.

Air quality prediction and management- In Beijing, China, machine learning analyzes meteorological and pollution data to predict air quality and manage emissions in real time reducing public health risks.

Wildlife conservation and poaching prevention-Smart Spatial Monitoring and Remote Tool (SMART) uses data from GPS tracking and camera traps to monitor wildlife and prevent poaching, particularly in African reserves. It helps rangers make informed patrol decisions and conserve endangered species.

Water Resource Management Department of Water Resources uses predictive analysis to manage water supply during droughts and allocate water resources efficiently across agricultural, industrial, and residual needs.

Challenges

Machine learning offers a faster alternative but lacks an understanding of intricate biophysical interactions.

Way Forward

Machine learning and biophysical models can be incorporated to form a machine that is faster, easier to deploy, and less computationally intensive. 

Conclusion 

Data science models can combine crop data with climate predictions to optimize fertilizer usage and predict crop yields, helping farmers increase production while minimizing environmental impacts. This can be achieved by developing a machine learning-based surrogate model that predicts GHG emissions and other climate change inhibitors and mitigation potential.