Open challenges in environmental data analysis and ecological complex systems

被引:7
|
作者
Hristopulos, D. T. [1 ]
Spagnolo, B. [2 ,3 ,4 ,5 ]
Valenti, D. [2 ,3 ,6 ]
机构
[1] Tech Univ Crete, Sch Elect & Comp Engn, Khania 73100, Greece
[2] Univ Palermo, Grp Interdisciplinary Theoret Phys, Dipartimento Fis & Chim Emilio Segre, Palermo, Italy
[3] CNISM, Unita Palermo, Palermo, Italy
[4] Lobachevsky State Univ, Radiophys Dept, Nizhnii Novgorod, Russia
[5] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy
[6] CNR, Ist Ric & Innovaz Biomed, CNR, IRIB, Via Ugo La Malfa 153, I-90146 Palermo, Italy
关键词
BIG DATA; GENE-EXPRESSION; COVARIANCE FUNCTIONS; GAUSSIAN-PROCESSES; NOISE; DYNAMICS; IMPACT; MODEL; EARTH; TIME;
D O I
10.1209/0295-5075/132/68001
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This letter focuses on open challenges in the fields of environmental data analysis and ecological complex systems. It highlights relations between research problems in stochastic population dynamics, machine learning and big data research, and statistical physics. Recent and current developments in statistical modeling of spatiotemporal data and in population dynamics are briefly reviewed. The presentation emphasizes stochastic fluctuations, including their statistical representation, data-based estimation, prediction, and impact on the physics of the underlying systems. Guided by the common thread of stochasticity, a deeper and improved understanding of environmental processes and ecosystems can be achieved by forging stronger interdisciplinary connections between statistical physics, spatiotemporal data modeling, and ecology. focus article Copyright (C) 2021 EPLA
引用
收藏
页数:7
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