On Machine Learning from Environmental Data
Authors: Kanevski Mikhail 
Published: March 2022

&lt;p&gt;&lt;span&gt;G&lt;/span&gt;eo- and environmental sciences produce a wide variety and numerous data &lt;span&gt;which are &lt;/span&gt;&lt;span&gt;extensively&lt;/span&gt; used both in fundamental research &lt;span&gt;on&lt;/span&gt; Earth processes and in important real-life decision-making. Most natural phenomena are non-linear, multivariate, highly variable and correlated at many spatio-temporal scales. Analysis and &lt;span&gt;treatment&lt;/span&gt; of such complex data and their integration/assimilation with science-based models is a &lt;span&gt;difficult&lt;/span&gt; problem. Contemporary machine learning (ML) proposes an important set of &lt;span&gt;effective &lt;/span&gt;approaches to address this problem &lt;span&gt;at all phases of the study&lt;/span&gt;.&lt;/p&gt;&lt;p&gt;Nowadays, &lt;span&gt;G&lt;/span&gt;eosciences are one of the major customers of ML ideas and technologies. To a large degree, it is connected to the local and global challenges facing humanity: sustainable development, biodiversity, social and natural hazards and risks, meteo- and climate forecasting, &lt;span&gt;remote sensing &lt;/span&gt;Earth observation, &lt;span&gt;etc&lt;/span&gt;. Despite being theoretically a universal modelling tool, the success of ML applications significantly depends on the problem formulation, quantity and quality of data and objectives of the study. Therefore, an efficient application of ML demands a good knowledge of the phenomena under study and a profound understanding of learning algorithms which can be achieved in close collaboration between experts in the corresponding domains.&lt;/p&gt;&lt;p&gt;In the current presentation, &lt;span&gt;t&lt;/span&gt;he study of &lt;span&gt;geo- and &lt;/span&gt;environmental data using &lt;span&gt;different &lt;/span&gt;machine learning &lt;span&gt;algorithms &lt;/span&gt;&lt;span&gt;is &lt;/span&gt;&lt;span&gt;reviewed&lt;/span&gt;. A problem-oriented approach, which follows a generic data-driven methodology, is &lt;span&gt;applied. &lt;/span&gt;&lt;span&gt;The methodology consists of several important steps, in particular,&lt;/span&gt; optimization &lt;span&gt;of monitoring&lt;/span&gt; and data collection, &lt;span&gt;comprehensive &lt;/span&gt;exploratory data analysis and &lt;span&gt;visualization, &lt;/span&gt;&lt;span&gt;feature engineering and relevant variables selection, &lt;/span&gt; modelling with careful validation and testing, &lt;span&gt;explanation&lt;/span&gt; &lt;span&gt;and communication &lt;/span&gt;of the results. &lt;span&gt;Advanced e&lt;/span&gt;xperimentation with data by &lt;span&gt;using&lt;/span&gt; &lt;span&gt;different supervised and unsupervised &lt;/span&gt;&lt;span&gt;ML &lt;/span&gt;&lt;span&gt;algorithms&lt;/span&gt; helps in better understanding of &lt;span&gt;original &lt;/span&gt;data &lt;span&gt;and constructed input feature space&lt;/span&gt;, obtaining &lt;span&gt;more &lt;/span&gt;reliable and robust results and making intelligent decisions. The presentation is accompanied by simulated and real data case studies from natural hazards &lt;span&gt;(avalanches, forest fires, landslides)&lt;/span&gt;, environmental risks &lt;span&gt;(pollution)&lt;/span&gt; &lt;span&gt;and &lt;/span&gt;renewable energy assessment. In conclusion, some general remarks and future perspectives are discussed.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;