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Open AccessArticle

Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

1
Department of Geography, Physical Geography, Christian-Albrechts-Universität zu Kiel, Ludewig-Meyn-Str. 14, 24118 Kiel, Germany
2
Department of Plant Diseases and Plant Protection, Institute of Phytopathology, Christian-Albrechts-Universität zu Kiel, Hermann-Rodewald-Str. 9, 24118 Kiel, Germany
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 44; https://doi.org/10.3390/ijgi9010044
Received: 8 November 2019 / Revised: 24 December 2019 / Accepted: 13 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Spatial Data Science)
Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted. View Full-Text
Keywords: machine learning; random forest; infestation forecast; powdery mildew machine learning; random forest; infestation forecast; powdery mildew
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Hamer, W.B.; Birr, T.; Verreet, J.-A.; Duttmann, R.; Klink, H. Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods. ISPRS Int. J. Geo-Inf. 2020, 9, 44.

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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3532695
    Description: The rendered R-code, which was used to generate the results of the article
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