Combining Local and Global Weather Data to Improve Forecast Accuracy for Agriculture
Authors
Year
2024
Published
Communication Papers of the 19th Conference on Computer Science and Intelligence Systems. Institute of Electrical and Electronics Engineers Inc., 2024. p. 77-82. Annals of Computer Science and Intelligence Systems. vol. 41. ISSN 2300-5963. ISBN 978-83-973291-0-2.
Type
Proceedings paper
Departments
Annotation
Accurate local weather forecasting is vital for farmers to optimize crop yields and manage resources effectively, but existing forecasts often lack the precision required locally. This study explores the potential of combining data from local weather stations with global forecasts and reanalysis data to improve the accuracy of local weather predictions. We propose integrating the HadISD data set, which contains data from 27 stations in the Czech Republic, with the Global Forecast System predictions and ERA5-Land reanalysis data. Our goal is to improve 24-hour weather forecasts using Multilayer Perceptrons, CatBoost, and Long Short-Term Memory neural networks. The findings demonstrate that combining local weather station data with global forecasts and incorporating ERA5-Land reanalysis data improves the accuracy of weather predictions in specific locations. Notably, using deep learning to estimate ERA5-Land data and including this estimation in the final model reduced the forecasting error by 59\%. This advancement holds promise in optimizing agricultural practices and mitigating weather-related risks in the region.
Highways in Warehouse Multi-Agent Path Finding: A Case Study.
Authors
Year
2022
Published
Proceedings of the 14th International Conference on Agents and Artificial Intelligence. Madeira: SciTePress, 2022. p. 274-281. ISBN 978-989-758-547-0.
Type
Proceedings paper
Departments
Annotation
Orchestrating warehouse sorting robots each transporting a single package from the conveyor belt to its destination is a NP-hard problem, often modeled Multi-agent path-finding (MAPF) where the environment is represented as a graph and robots as agents in vertices of the graph. However, in order to maintain the speed of operations in such a setup, sorting robots must be given a route to follow almost at the moment they obtain the package, so there is no time to perform difficult offline planning. Hence in this work, we are inspired by the approach of enriching conflict-based search (CBS) optimal MAPF algorithm by so-called highways that increase the speed of planning towards on-line operations. We investigate whether adding highways to the underlying graph will be enough to enforce global behaviour of a large number of robots that are controlled locally. If we succeed, the slow global planning step could be omitted without significant loss of performance.