IEEE Young Professional Studies Big Data to Predict Climate Variations

IEEE Young Professional Pooja Shah answers questions on how big data is being used to solve pressing problems in mitigating climate change.

Pooja Shah is a member of the IEEE Young Professionals Climate and Sustainability Task Force. She is currently pursuing a Ph.D. in Microclimate Change Policy framing for Urban Areas. As a young researcher in Urban Microclimate Change, Shah is committed to using purpose as a driving force for impactful action.

IEEE invited Shah to address big data and how it supports climate and sustainability research and action.

IEEE: In what specific ways is big data being utilized to combat climate change, and what are some notable examples of its applications in this field?

Pooja Shah: Big data holds immense potential across multiple facets of climate-change research and analysis. One such application involves scrutinizing temperature fluctuations across a given region, enabling us to comprehend the extent of climate variations and their implications. Other examples include using big data to investigate shifts in precipitation patterns, providing crucial insights into changing weather dynamics and their impact on ecosystems; to visualize the evolution of river meandering, thereby comprehending the intricate alterations in river courses and their potential consequences for surrounding landscapes; to monitor coastal erosion, shedding light on the factors contributing to the loss of valuable shorelines and informing mitigation strategies.

Forest fires, which pose a significant threat to ecosystems and human settlements, can also be effectively analyzed using big data. By processing extensive datasets, we can identify patterns, assess contributing factors, and develop proactive measures to prevent or mitigate such catastrophic events. Big data analytics also play a pivotal role in understanding and managing flooding events. By examining historical data and real-time monitoring, we can identify vulnerable areas, predict flood patterns, and implement preemptive measures to safeguard lives and infrastructure.

These examples merely scratch the surface of the countless applications of big data in climate-change research. By harnessing its power, we can gain deeper insights into environmental phenomena, leading to informed decision-making and more effective strategies for mitigating the impacts of climate change.

IEEE: How does the analysis of large datasets contribute to better understanding and predicting climate patterns, and how does this knowledge aid in developing effective strategies to mitigate and adapt to climate change?

Shah: Big data plays a pivotal role in comprehending historical occurrences. Take, for instance, the analysis of temperature fluctuations across a specific geographical area. By harnessing extensive historical datasets, one can discern the intricate shifts, patterns, and seasonal variations in temperature through big data analytics.

Moreover, these datasets enable the exploration and prediction of future temperature trends. Consequently, urban-planning authorities can leverage this invaluable information to devise strategic policies aimed at mitigating temperature escalation within regions and designing heat-dissipation systems appropriately. Big data analytics can be extended to diverse applications, including investigating the correlation between urbanization and its impact on precipitation, pollution, and other relevant factors.

IEEE: What are the key challenges and ethical considerations associated with the use of big data in addressing climate change? How can we ensure the responsible and equitable use of data-driven approaches in the fight against climate change?

Shah: The utilization of big data presents notable hurdles, prominently centered around the need for advanced infrastructure capable of accommodating the vast storage, processing, and analysis requirements.

In addition, the availability of authorized and licensed software tools for effectively visualizing and predicting data becomes a critical concern, particularly when dealing with remote-sensing datasets. These challenges assume even greater significance within developing nations, where the establishment of robust data-driven systems encounters substantial obstacles.

IEEE: How does AI contribute to the analysis of big data in the context of climate change? What specific AI techniques or algorithms are commonly employed to extract insights and patterns from vast environmental datasets?

Shah: AI significantly contributes to the analysis of big data in the context of climate change, from the perspective of a remote-sensing scientist. AI techniques such as machine learning and deep learning enable the extraction of insights and patterns from vast environmental datasets.

Machine-learning algorithms facilitate predictive modeling, allowing for the forecasting of climate trends and the identification of anomalies. Deep-learning techniques, such as Convolutional Neural Networks (CNNs), excel in image-analysis tasks, extracting valuable information from satellite imagery.

Data-mining techniques unveil hidden patterns and relationships within large datasets, shedding light on important factors influencing climate change. Natural Language Processing (NLP) techniques analyze textual data, providing insights from climate reports and scientific literature. Ensemble modeling combines various AI techniques to enhance climate predictions. In summary, AI empowers remote-sensing scientists to extract valuable insights, predict future climate trends, and make informed decisions for climate-change mitigation and environmental management. The majorly used algorithm to use and predict dataset is prophet, and other open source algorithms available on the Google Earth engine.

Big data is a vital tool in addressing the Earth’s climate condition. It provides an abundance of opportunities to better understand our world. Engineers have developed systems that track and analyze data from various aspects of Earth’s climate system and ultimately contribute to more informed decision-making. Dr. Jean-Philippe Montillet and Pooja Shah are just two of the many engineers and data scientists working in this growing field of study.

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