A Gene-Random Forest Model for Meteorological Drought Prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s00024-023-03283-1.pdf
Reference43 articles.
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2. Aghelpour, P., Mohammadi, B., Mehdizadeh, S., Bahrami-Pichaghchi, H., & Duan, Z. (2021). A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stochastic Environmental Research and Risk Assessment, 35(12), 2459–2477.
3. Ahmadi, F., Mehdizadeh, S., & Mohammadi, B. (2021). Development of bio-inspired-and wavelet-based hybrid models for reconnaissance drought index modeling. Water Resources Management, 35(12), 4127–4147.
4. Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020, April). Hessian complexity measure for genetic programming-based imputation predictor selection in symbolic regression with incomplete data. In: European Conference on Genetic Programming (Part of EvoStar) (pp. 1–17). Springer, Cham
5. Alizamir, M., Heddam, S., Kim, S., & Mehr, A. D. (2021). On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: case studies of river and lake in USA. Journal of Cleaner Production, 285, 124868.
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