World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
5197
World Ranking
13506
National Ranking
215

Overview

Mikhail Kanevski is affiliated with the University of Lausanne in Switzerland. Their research spans multiple fields including Computer Science and Engineering, with a particular focus on subfields such as Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing, Economics and Econometrics, and Aerospace Engineering.

Their work addresses several main topics including:

  • Time Series Analysis and Forecasting
  • Complex Systems and Time Series Analysis
  • Energy Load and Power Forecasting
  • Wind Energy Research and Development
  • Electric Power System Optimization
  • Machine Learning and Extreme Learning Machines (ELM)
  • Machine Learning and Algorithms

Kanevski has collaborated frequently with several coauthors, including Federico Amato, Fabian Guignard, Mohamed Laib, Alina Walch, and Nahid Mohajeri.

Their recent publications demonstrate a range of research outputs across various scientific journals:

  • "Analysis of air pollution time series using complexity-invariant distance and information measures," 2020, Physica A Statistical Mechanics and its Applications
  • "Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential," 2022, Stochastic Environmental Research and Risk Assessment
  • "Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences," 2020, Frontiers in Earth Science
  • "Uncertainty quantification in extreme learning machine: Analytical developments, variance estimates and confidence intervals," 2021, Neurocomputing
  • "Unsupervised learning of Swiss population spatial distribution," 2021, PLoS ONE

Kanevski's work has been published in several venues, with frequent contributions to arXiv (Cornell University), Frontiers in Earth Science, Stochastic Environmental Research and Risk Assessment, Physica A Statistical Mechanics and its Applications, and Neurocomputing.

Best Publications

  • A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

    Devis Tuia;Michele Volpi;Loris Copa;Mikhail Kanevski

  • Active Learning Methods for Remote Sensing Image Classification

    D. Tuia;F. Ratle;F. Pacifici;M.F. Kanevski

  • Supervised change detection in VHR images using contextual information and support vector machines

    Michele Volpi;Devis Tuia;Francesca Bovolo;Mikhail F. Kanevski

  • Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

    Natan Micheletti;Loris Foresti;Sylvain Robert;Michael Leuenberger

  • Machine Learning for Spatial Environmental Data: Theory, Applications, and Software

    Mikhail Kanevski;Alexei Pozdnoukhov;Vadim Timonin

  • Learning Relevant Image Features With Multiple-Kernel Classification

    Devis Tuia;Gustavo Camps-Valls;Giona Matasci;Mikhail Kanevski

  • Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification

    Giona Matasci;Michele Volpi;Mikhail Kanevski;Lorenzo Bruzzone

  • Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines

    D. Tuia;F. Pacifici;M. Kanevski;W.J. Emery

  • Environmental data mining and modeling based on machine learning algorithms and geostatistics

    Mikhail F. Kanevski;Roman Parkin;Aleksey Pozdnukhov;Aleksey Pozdnukhov;Vadim Timonin

  • Wildfire susceptibility mapping: deterministic vs. stochastic approaches

    Michael Leuenberger;Joana Parente;Marj Tonini;Mário Gonzalez Pereira;Mário Gonzalez Pereira

  • Unsupervised Change Detection With Kernels

    Michele Volpi;Devis Tuia;Gustavo Camps-Valls;Mikhail Kanevski

  • Data-driven mapping of the potential mountain permafrost distribution

    Nicola Deluigi;Christophe Lambiel;Mikhail Kanevski

  • SVM-Based Boosting of Active Learning Strategies for Efficient Domain Adaptation

    G. Matasci;D. Tuia;M. Kanevski

  • Scan statistics analysis of forest fire clusters

    Devis Tuia;Fréderic Ratle;Rosa Lasaponara;Luciano Telesca

  • Extreme Learning Machines for spatial environmental data

    Michael Leuenberger;Mikhail Kanevski

  • Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal

    Federico Amato;Marj Tonini;Beniamino Murgante;Mikhail F. Kanevski

  • Spatio-temporal avalanche forecasting with Support Vector Machines

    Alexei Pozdnoukhov;Giona Matasci;Mikhail Kanevski;Ross S Purves

  • Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images

    D. Fasbender;D. Tuia;P. Bogaert;M. Kanevski

  • Applying machine learning methods to avalanche forecasting

    Alexei Pozdnoukhov;Ross S Purves;Mikhail Kanevski

  • Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy

    Mikhail Kanevski

  • Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network

    Mohamed Laib;Luciano Telesca;Mikhail Kanevski

  • A novel framework for spatio-temporal prediction of environmental data using deep learning

    Federico Amato;Fabian Guignard;Sylvain Robert;Mikhail Kanevski

Frequent Co-Authors

Devis Tuia
Devis Tuia École Polytechnique Fédérale de Lausanne
Luciano Telesca
Luciano Telesca National Research Council (CNR)
Stéphane Canu
Stéphane Canu Institut National des Sciences Appliquées de Rouen
Lorenzo Bruzzone
Lorenzo Bruzzone University of Trento
William J. Emery
William J. Emery University of Colorado Boulder
Ross S. Purves
Ross S. Purves University of Zurich
Jordi Muñoz-Marí
Jordi Muñoz-Marí University of Valencia
Enrico Benetto
Enrico Benetto Luxembourg Institute of Science and Technology
George P. Petropoulos
George P. Petropoulos National and Kapodistrian University of Athens
Patrick C. M. Wong
Patrick C. M. Wong Chinese University of Hong Kong

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