Special Issue in Mathematical Geosciences


Guest Editors:
Sandra De Iaco (University of Salento, Italy)
Dionissios Hristopulos (Technical University of Crete, Greece)
Guang Lin (Purdue University, United States of America)

This special issue will explore the connections between Geostatistics and Machine Learning, in particular deep learning, and their applications in spatial data processing and modeling. We welcome critical and synthetic reviews of relevant contributions in the literature as well as papers that provide new ideas regarding deep neural networks, kernel classes and hybrid models for mapping problems and the classification of environmental and pollution data; such contributions may include the use of automatic algorithms and optimization (design/redesign) of monitoring networks. Novel applications and comparative studies of geostatistical and machine learning methods are also appreciated.


Main topics:
• Integrated/hybrid spatial models for prediction and simulation
• Spatio-temporal modeling and prediction
• Classification models
• Inverse problems
• Big data and data mining modeling
• Software and routines
• Deep learning applications to spatial problems



A tentative title and an abstract (300–500 words) should be sent to the Guest Editors by March 15th, 2020. Full manuscripts should respect the journal’s guidelines for authors and be submitted online using the Editorial Manager system.
• Paper submission before: July 15th, 2020
• Return of reviews to authors before October 15th, 2020
• Submission of final papers deadline: January 31st, 2021
• Publication: Mid 2021

Submit Papers online through the journal’s website

When submitting, you must choose, under ‘Select Article Type,’ the SI: “Geostatistics and Machine Learning.”
Submitted manuscripts must fully comply with the journal’s Instructions for Authors in preparing manuscripts.

For inquiries please contact the Guest Editors:
Sandra De Iaco                        Dionissios Hristopulos    Guang Lin
sandra.deiaco@unisalento.it    dionisi@mred.tuc.gr       guanglin@purdue.edu