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Data and AI are powerful tools in the fight against climate change. Sharpen your skills while helping to protect society and the planet. Join the EY Open Science Data Challenge at challenge.ey.com/2024 #EY #BetterWorkingWorld #AI #OpenScience

Data and AI are powerful tools in the fight against climate change. Sharpen your skills while helping to protect society and the planet. Join the EY Open Science Data Challenge at challenge.ey.com/2024 #EY #BetterWorkingWorld #AI #OpenScience

Data and AI are powerful tools in the fight against climate change. Sharpen your skills while helping to protect society and the planet. Join the EY Open Science Data Challenge at challenge.ey.com/2024 #EY #BetterWorkingWorld #AI #OpenScience

Data and AI are powerful tools in the fight against climate change. Sharpen your skills while helping to protect society and the planet. Join the EY Open Science Data Challenge at challenge.ey.com/2024 #EY #BetterWorkingWorld #AI #OpenScience

In the era of climate change, data science and machine learning have become indispensable in identifying trends and forecasting future risks. Below are 4 types of data science and ML tools and methods we can use in climate risk assessment. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗹𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 like Pandas, Matplotlib, and Scikit-learn offer a powerful environment for data manipulation, visualization, and machine learning, allowing us to process climate data and recognize patterns. 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘁𝗼𝗼𝗹𝘀 like ArcGIS and QGIS enable us to visualize flood patterns and understand geographical vulnerabilities. 𝗧𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 can help us extrapolate future events based on past data. Combining this with 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 like decision trees and random forests, we can predict various climate risk scenarios. 𝗗𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 like neural networks and LSTM networks process large data sets and are ideal for complex predictions. By analyzing weather patterns over time, they can provide insights into future climate conditions. Leveraging these tools, we are better equipped to understand, anticipate, and prepare for climate risks, and contribute to a more resilient future. Which tools and methods have you used the most? #DataScience #MachineLearning #ClimateChange #RiskAssessment

One way to accelerate innovation in #sustainability is to focus the brightest minds in data science on a single social or environmental challenge. This is the driving force behind our Global Data Science Challenge, which this year focused on using #AI to identify insect species. Here, our 2023 winners share their experiences from the competition and their plans for the future: https://bit.ly/48a2TVj

Unlocking Climate Patterns: Deep Learning’s Contribution to Advanced Modeling 📢 Exciting News! 🌍 Unlocking Climate Patterns: Deep Learning’s Contribution to Advanced Modeling is now live on our blog! 🚀 Climate change is one of the most pressing challenges we face today. To effectively mitigate its impacts, understanding and predicting climate patterns is crucial. Our latest blog post explores how recent advancements in deep learning can revolutionize climate modeling and usher in new insights into climate patterns. 🌦️🌡️ Learn about the power of deep learning algorithms in capturing complex climate processes and improving climate predictions. Discover how they enhance the accuracy and resolution of traditional models, address model biases, and provide a holistic view of the Earth’s climate system. 🌐 But that's not all! Deep learning also holds the potential to unlock previously unknown relationships and patterns in the climate system, helping scientists better understand the mechanisms driving climate change and identify tipping points. ⚡ Join us on this exciting journey of unlocking climate patterns with deep learning by reading the full blog post here: [Unlocking Climate Patterns: Deep Learning’s Contribution to Advanced Modeling](https://ift.tt/CMmt4A7) 📝 #ClimateChange #DeepLearning #ClimateModeling #UnlockingClimatePatterns #NewBlogPost The post Unlocking Climate Patterns: Deep Learning’s Contribution to Advanced Modeling first appeared on InstaDataHelp Analytics Services. https://ift.tt/CMmt4A7

🌍 Every year, I look forward to Capgemini's Global Data Science Challenge, which challenges Capgemini's data scientists worldwide to propose solutions that build a sustainable future for the planet using AI.   And I have to say – the 2023 edition didn't disappoint! This year, Capgemini collaborated with Naturalis Biodiversity Center and Amazon Web Services (AWS) on how to protect insect ecosystems through #DataAndAI. 🐝 The focus question of the hackathon was: Could AI and ML reliably identify insect species through their chirps alone? 🦗 Competing teams had to design a model that could accurately classify cicada and grasshopper sound recordings into one of 66 sub-species. The team with the highest identification rate would be the winner. I just read this article about how the winning team hopped to the top of the challenge, and it's fascinating! For example, I found it so cool that data scientist, Dominik, got the opportunity to join the hackathon on his first day at work at Capgemini! Of course, he flew at the chance – and his team won!    Check out the article 👉 https://lnkd.in/daDFxUrh #LifeAtCapgemini #4PositiveFutures #Biodiversity #GetTheFutureYouWant #AWSreInvent

From Data to Insights: Deep Learning’s Role in Climate Modeling 📢 Exciting news! Check out our latest blog post on "From Data to Insights: Deep Learning's Role in Climate Modeling." 🌍🔬 Climate change is a pressing challenge, and understanding climate patterns is crucial for effective strategies. Deep learning has emerged as a powerful tool in climate modeling, enabling scientists to extract valuable insights from vast amounts of data. In this article, we explore the role of deep learning in climate modeling and its potential to revolutionize our understanding of climate dynamics. 👉 Read the full article here: [Link to Blog Post](https://ift.tt/lzdykc2) Discover how deep learning can improve extreme weather prediction, climate pattern recognition, cloud and precipitation modeling, and climate data assimilation. While deep learning holds great promise, there are challenges and limitations that need further research. Combine the power of deep learning with traditional climate modeling approaches to unlock new insights and pave the way for a more sustainable and resilient future. Don't miss out on this fascinating read! 📚🌟 #climatechange #datascience #deeplearning #climatemodeling #sustainability https://ift.tt/lzdykc2

Developments in AI and geospatial modeling are helping businesses predict and prepare for climate change. Read the article to learn how you can help prepare your business.