International Conference on Machine Learning Methods in Ecological and Environmental Sciences ICMLMEES on September 16-17, 2026 in Rome, Italy

Advances in Climate Science
Application of machine learning to agriculture, hydrology and atmospheric modeling
Applications in environmental sciences
Applications of extreme learning machines, gaussian process regression models, genetic programming algorithms, support vector regressions and neural networks
Applications of Machine Learning in Environmental Engineering
Artificial Intelligence Methods in the Environmental Sciences
Classical data analysis
Combining geostatistics and remote sensing
Combining machine learning and remote sensing
Comparison of dynamic (physical) models versus other machine learning models for predictive modeling
Comparison of hydrological models with machine learning models
Datasets in Climate/Environmental Science
Downscaling of Global Climate Model outputs using machine learning (data-driven) algorithms
Ecological Data Analysis with Machine Learning Methods
Feed-forward neural network models
Geoinformatics for modeling of climate data and analysis
Geostatistics and Machine Learning Applications in Climate and Environmental Sciences
Kernel methods
Learning and generalization
Linear multivariate statistical analysis
Machine Learning Algorithms for Environmental Applications
Machine Learning and Data Mining for Environmental Sciences
Machine learning applications in climate and environmental science
Machine Learning Applications in Habitat Suitability Modeling
Machine Learning in Ecological Science and Environmental Policy
Machine Learning Methods for Ecological Applications
Machine Learning Methods in the Environmental Sciences
Machine Learning Techniques in Ecology and Earth Science
Modeling of rainfall, floods, streamflow, drought, heatwaves, flood and other natural disasters using machine learning
Nonlinear canonical correlation analysis
Nonlinear classification
Nonlinear optimization
Nonlinear principal component analysis
Nonlinear regression
Sampling optimization in climate/environmental science
Software advances in spatial analysis
Spatio-temporal interpolation of climate/environmental data
Spatio-temporal variations of climate variables
Time series analysis
Training, testing and error assessment issues in predictive modeling for environmental applications