@misc{wichert_data_analysis_2022, 
author={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., zu Castell, W.},
title={Data Analysis and Exploration with Computational Approaches},
year={2022},
howpublished = {book part},
doi = {https://doi.org/10.1007/978-3-030-99546-1_4},
abstract = {Artificial intelligence and machine learning (ML) methods are increasingly applied in Earth system research, for improving data analysis, and model performance, and eventually system understanding. In the Digital Earth project, several ML 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 and for environmental health; point-to-space extrapolation of varying observed quantities; 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.},
note = {Online available at: \url{https://doi.org/10.1007/978-3-030-99546-1_4} (DOI). 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.; zu Castell, W.: Data Analysis and Exploration with Computational Approaches. In: Bouwer, L.; Dransch, D.; Ruhnke, R.; Rechid, D.; Frickenhaus, S.; Greinert, J. (Ed.): Integrating Data Science and Earth Science : Challenges and Solutions. Cham: Springer. 2022. 29-53. DOI: 10.1007/978-3-030-99546-1_4}}