Leptospirosis modelling using hydrometeorological indices and random forest machine learning
Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use o...
Saved in:
Main Authors: | Jayaramu, Veianthan, Zulkafli, Zed, De Stercke, Simon, Buytaert, Wouter, Rahmat, Fariq, Abdul Rahman, Ribhan Zafira, Ishak, Asnor Juraiza, Tahir, Wardah, Ab Rahman, Jamalludin, Mohd Fuzi, Nik Mohd Hafiz |
---|---|
Format: | Article |
Published: |
Springer Science and Business Media
2023
|
Online Access: | http://psasir.upm.edu.my/id/eprint/109550/ https://link.springer.com/article/10.1007/s00484-022-02422-y?error=cookies_not_supported&code=b68bc8b6-2a6c-44d6-9266-49d454508008 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Leptospirosis modelling using hydrometeorological indices and random forest machine learning
by: Jayaramu, Veianthan, et al.
Published: (2023) -
Supervised feature selection using principal component analysis
by: Rahmat, Fariq, et al.
Published: (2023) -
Prediction model of leptospirosis occurrence for Seremban (Malaysia) using meteorological data
by: Rahmat, Fariq, et al.
Published: (2019) -
Hydrometeorological monitoring for hydropower reservoirs in remote areas
by: Basri, H., et al.
Published: (2020) -
Hydrometeorological monitoring for hydropower reservoirs in remote areas
by: Basri H., et al.
Published: (2023)