Version 1
: Received: 11 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (14:51:04 CEST)
How to cite:
Schließer, J.; Kirschenbaum, A.; Zinke-Wehlmann, C. Forecasts of Extreme Meteorological Events Based on Remote Sensed Data Using Machine Learning and Their Effects on Agricultural Productivity. Preprints2024, 2024060794. https://doi.org/10.20944/preprints202406.0794.v1
Schließer, J.; Kirschenbaum, A.; Zinke-Wehlmann, C. Forecasts of Extreme Meteorological Events Based on Remote Sensed Data Using Machine Learning and Their Effects on Agricultural Productivity. Preprints 2024, 2024060794. https://doi.org/10.20944/preprints202406.0794.v1
Schließer, J.; Kirschenbaum, A.; Zinke-Wehlmann, C. Forecasts of Extreme Meteorological Events Based on Remote Sensed Data Using Machine Learning and Their Effects on Agricultural Productivity. Preprints2024, 2024060794. https://doi.org/10.20944/preprints202406.0794.v1
APA Style
Schließer, J., Kirschenbaum, A., & Zinke-Wehlmann, C. (2024). Forecasts of Extreme Meteorological Events Based on Remote Sensed Data Using Machine Learning and Their Effects on Agricultural Productivity. Preprints. https://doi.org/10.20944/preprints202406.0794.v1
Chicago/Turabian Style
Schließer, J., Amit Kirschenbaum and Christian Zinke-Wehlmann. 2024 "Forecasts of Extreme Meteorological Events Based on Remote Sensed Data Using Machine Learning and Their Effects on Agricultural Productivity" Preprints. https://doi.org/10.20944/preprints202406.0794.v1
Abstract
Climate change poses new challenges for agricultural production as an increase in temperature also brings an increase in frequency and intensity of droughts. We study the occurrences of droughts in recent years as well as their effect on agricultural yields in combination with other factors such as winter frost. Furthermore we run experiments that aim to predict future drought events in order to provide insights whether current agricultural products will remain viable long-term and to indicate short-term drought risks.Our study area consists of the South-Moravian region in the Czech Republic and Saxony in Germany with the timeframe ranging from 1990−2022. For the effect on agricultural yields we use statistical methods such as regression and correlation.We train a Long-Short Term Memory (LSTM) neural net on data for the Standardised Precipitation-Evapotranspiration Index (SPEI) in order to predict future events. Our results indicate that the increased severity of droughts in recent years didn’t yet cause yield losses in our researched area. Our predictions with the LSTM look promising, but will need more data for future use-cases in order to increase accuracy and robustness.
Keywords
Drought; Frost; SPEI; LSTM
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
