In 1851, Foucault experimented using a pendulum, which became widely known as the Foucault Pendulum, to demonstrate the rotation of the Earth on its axis. It offered empirical evidence of the Earth's rotation.

Earth system sciences are currently emphasizing the need for innovation to improve accuracy, increase the intelligence level of models, expand operational capabilities, and decrease costs in various subdomains. This urgency arises from the rapidly growing datasets and the potential of the AI revolution. 

Earth AI

Geoscientists spearheaded the creation of tools that connect geoscientific data with AI models, effectively closing the gaps between the two. The advancements in both theoretical understanding and technological framework will propel geoscience onto the subsequent stage: Earth Artificial Intelligence (Earth AI). The researchers envision Earth AI as a comprehensive integration of systems designed to autonomously monitor and predict natural phenomena, facilitate the adaptation of human society to environmental changes, provide guidance for formulating environmentally conscious policies and decisions, and safeguard against geohazards. Earth AI will serve as a crucial instrument to address significant obstacles such as the rapidly growing population, ensuring food security, and mitigating climate change.

AI for Earth

In addition, AI approaches assist researchers by enabling a scalable method for gathering and analyzing planetary data. AI for Earth is a Microsoft-powered effort designed to address difficulties related to the natural environment. The AI for Earth project integrates Microsoft cloud technologies with AI techniques to advance the sustainable development of the Earth. Microsoft is providing grants to initiatives and teams with AI for Earth's technical resources to address future challenges as a global community. 

Global challenges

AI has become a practical tool in various scientific fields on Earth. AI has the potential to be highly beneficial in saving the planet. The applications of AI can be categorized into the following domains on Earth:

• AI for Climate Change
• AI for Conservation
• AI for Agriculture
• AI for Water

The following is an extensive synopsis of the main approaches used to investigate Earth through AI.

Earthquake

The Holy Grail of seismology—earthquake forecasting—has drawn attention from various AI applications. For this purpose, feedforward and recurrent neural networks are two of the most popular machine-learning techniques. These methods use neural networks to predict the future location and magnitude of earthquakes within a time or spacetime window; these predictions are frequently based on time series of past earthquake characteristics, such as magnitude, focus location, or occurrence time. 

Volcano

For many years, the monitoring, mitigation, and reduction of risks related to volcanic hazards in volcanology have been accomplished through the manual analysis of gas emissions, deformation measurement, and seismic signals. A significant use of AI in volcano monitoring is to differentiate between seismic volcanic tremors and related occurrences, including earthquakes, landslides, lava fountains, wind, and thunder.

Landslides

Every year, landslides in mountainous regions result in losses of billions of dollars. Most AI applications in landslide investigations have focused on risk assessment. ML techniques like logistic regression, ANN, and SVM have been tested in landslide susceptibility mapping. 

Hydrosphere

AI techniques and applications have been highly beneficial to hydrosphere research. This section will cover three topics in detail: groundwater, surface water, and rainfall.

Rainfall

Understanding intricate nonlinear patterns in the data is necessary for forecasting rainfall. Two methods proposed for rainfall forecasting use RNNs with SVMs or Singular Spectrum Analysis (SSA) and SVMs in combination. The ANN, KNN, and radial basis SVM models were added to this multi-model method to forecast the daily or monthly precipitation.

Conclusion

We live in exciting times, as new technologies like AI are making it possible to address some of the planet's significant issues. It's time to use AI to benefit the environment. It's time to reconsider our options for protecting Earth. How can we all improve? What is the potential of current technology? Furthermore, students must create more creative AI-based solutions.