International Conference on Environmental Applications of Machine Learning

ICEAML

7th Apr – 8th Apr 2027 Ottawa, Canada

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

Connect with global educators & researchers

Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

Credit Card

Debit Card

Net Banking

Cash Card

Unified Payments

Select Registration Mode

Standard / Physical Participation USD

Join us on-site for keynotes, talks, workshops, networking, conference kit and certificate.

Virtual Participation USD

Participate online via live streaming. Includes live access to all sessions, digital materials and certificate.

Participant Details

Full Name *

Email Address *

Mobile Number with Country Code *

City *

State *

Country *

Zip / Postal Code *

Address *

Coupon Code

Terms & Condition

I agree to the Terms & Conditions and Refund & Cancellation Policy.

Read the full Terms & Conditions

Registration Summary

Conference ICEAML

Date 7th Apr – 8th Apr 2027

Location Ottawa, Canada

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

Calculated based on 5.8% of the registration fee

Discount USD 0.00

Total Payable USD 0.00

100% Secure Payment

Your payment information is encrypted and processed securely.

Conference Session Tracks

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

01 Machine Learning for Climate Modeling +

This track focuses on the application of machine learning techniques in climate modeling to enhance predictive accuracy and understanding of climate dynamics. Contributions may include novel algorithms, data assimilation methods, and case studies demonstrating the impact of machine learning on climate predictions.

SDG 13 SDG 15 SDG 17

02 Pollution Prediction and Control +

This session aims to explore innovative machine learning approaches for predicting pollution levels and identifying sources of environmental contaminants. Papers may address the integration of sensor data and machine learning models to develop real-time pollution monitoring systems.

SDG 11 SDG 12 SDG 13

03 Environmental Monitoring through Remote Sensing +

This track highlights the use of machine learning in processing and analyzing remote sensing data for environmental monitoring. Researchers are encouraged to present methodologies that improve the extraction of environmental information from satellite imagery and aerial surveys.

SDG 15 SDG 13 SDG 11

04 Ecosystem Analysis and Biodiversity Assessment +

This session will cover the application of machine learning in analyzing ecosystems and assessing biodiversity. Contributions may include studies on species distribution modeling, habitat suitability, and the use of ecological data mining techniques.

SDG 15 SDG 14 SDG 13

05 Predictive Analytics for Resource Optimization +

This track focuses on the use of predictive analytics powered by machine learning to optimize resource management in environmental contexts. Papers may explore applications in water resource management, energy efficiency, and sustainable land use planning.

SDG 6 SDG 7 SDG 12

06 Supervised Learning in Environmental Data Science +

This session will delve into the application of supervised learning techniques to solve complex environmental problems. Researchers are invited to present case studies and methodologies that demonstrate the effectiveness of these techniques in various environmental domains.

SDG 13 SDG 15 SDG 11

07 Unsupervised Learning for Environmental Insights +

This track aims to explore the potential of unsupervised learning methods in uncovering hidden patterns and insights from environmental data. Contributions may include clustering techniques, dimensionality reduction, and anomaly detection in ecological datasets.

SDG 15 SDG 14 SDG 13

08 Deep Learning Applications in Environmental Science +

This session will showcase cutting-edge deep learning methods applied to various environmental challenges. Topics may include image recognition for ecological monitoring, time series forecasting for climate data, and advanced neural network architectures for environmental modeling.

SDG 13 SDG 15 SDG 11

09 Anomaly Detection in Environmental Monitoring +

This track focuses on the development and application of anomaly detection techniques to identify unusual patterns in environmental data. Papers may discuss methodologies for detecting anomalies in sensor data, climate records, and ecological indicators.

SDG 11 SDG 13 SDG 15

10 Weather Forecasting with Machine Learning +

This session will explore the integration of machine learning techniques in enhancing weather forecasting models. Contributions may include novel algorithms, data fusion methods, and case studies demonstrating improved forecasting accuracy.

SDG 13 SDG 11 SDG 15

11 Environmental Risk Assessment using Machine Learning +

This track aims to discuss the role of machine learning in assessing environmental risks and vulnerabilities. Researchers are invited to present frameworks and models that quantify risks related to climate change, pollution, and ecological degradation.

SDG 13 SDG 15 SDG 11