Chapter 4: Data Analysis and Exploration with Computational Approaches
| Autor: | Wichert, V., Bouwer, L., Abraham, N., Brix, H., Callies, U., González Ávalos, E., Marien, L., Matthias, V., Michaelis, P., Rabe, D., Rechid, D., Ruhnke, R., Scharun, C., Valizadeh, M., Vlasenko, A., Graf zu Castell-Rüdenhausen, W. |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Zdroj: | Integrating Data Science and Earth Science Challenges and Solutions SpringerBriefs in Earth System Sciences |
| Popis: | Artificial intelligence and machine learning (ML) methods are increasinglyappliedinEarthsystemresearch,forimprovingdataanalysis,andmodelperformance,andeventuallysystemunderstanding.IntheDigitalEarthproject,severalML approaches have been tested and applied, and are discussed in this chapter. These include data analysis using supervised learning and classification for detection of river levees and underwater ammunition; process estimation of methane emissions andforenvironmentalhealth;point-to-spaceextrapolationofvaryingobservedquantities; anomaly and event detection in spatial and temporal geoscientific datasets. We present the approaches and results, and finally, we provide some conclusions on the broad applications of these computational data exploration methods and approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|