Abstract
Climate change affects the environment in several ways, including rising temperatures, sea level rise, and more frequent and intense extreme weather events such as heat waves, hurricanes, and droughts. Climate change also impacts agriculture, as changing temperature and precipitation patterns can alter the growing season, reduce crop yields, and increase the risk of pests and diseases.
The economy is affected by the direct impacts of climate change, such as damage to infrastructure, decreased productivity and crop yields, and increased energy and insurance costs. Indirect effects include supply chain disruptions and reduced economic activity in affected areas. Climate change also significantly impacts human health, such as increased air pollution, heat stress, the spread of disease through insects and pests, and increased risk of food and water insecurity. These factors can lead to a range of health problems, including respiratory diseases, cardiovascular disease, and malnutrition.
The ability to efficiently transmit and rapidly process vast amounts of data has become almost indispensable to our daily lives. The techniques of computational statistics provide an interface between data and its meaningful inferences. Statistical data science is rooted in computational statistics, which is also the primary foundation of machine learning and deep learning algorithms and hence provides an up-and-coming solution to transform data into practical knowledge. So, studying the different computational and statistical data science techniques and their applications for agriculture, economy, environment, and health becomes necessary.
This book aims to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modeling, statistical data science techniques, and machine learning algorithms combined with describing their applications focusing on measuring the impacts of climate change on various fields, including agriculture, economy, environment, and health.
The economy is affected by the direct impacts of climate change, such as damage to infrastructure, decreased productivity and crop yields, and increased energy and insurance costs. Indirect effects include supply chain disruptions and reduced economic activity in affected areas. Climate change also significantly impacts human health, such as increased air pollution, heat stress, the spread of disease through insects and pests, and increased risk of food and water insecurity. These factors can lead to a range of health problems, including respiratory diseases, cardiovascular disease, and malnutrition.
The ability to efficiently transmit and rapidly process vast amounts of data has become almost indispensable to our daily lives. The techniques of computational statistics provide an interface between data and its meaningful inferences. Statistical data science is rooted in computational statistics, which is also the primary foundation of machine learning and deep learning algorithms and hence provides an up-and-coming solution to transform data into practical knowledge. So, studying the different computational and statistical data science techniques and their applications for agriculture, economy, environment, and health becomes necessary.
This book aims to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modeling, statistical data science techniques, and machine learning algorithms combined with describing their applications focusing on measuring the impacts of climate change on various fields, including agriculture, economy, environment, and health.
| Original language | English |
|---|---|
| Place of Publication | UK |
| Publisher | John Wiley & Sons |
| Number of pages | 416 |
| Volume | 1 |
| Edition | 1 |
| Publication status | Accepted/In press - 01 Sept 2026 |
