An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction
- 10.2991/ijcis.d.191126.001How to use a DOI?
- Extreme Learning Machine; Gene Expression Programming; Quantitative precipitation prediction; Rainfall prediction; Soft computing
Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches. This work presents a scheme to reduce the risk of Extreme Learning Machine (ELM) modeling error using Gene Expression Programming (GEP) to improve the prediction performance, and develops an ELM-GEP hybrid model for regional daily quantitative precipitation prediction. In this study, firstly, we use ELM for modeling the data sample of daily rainfall to construct a main model. Secondly, we use GEP for modeling the error of the main model as a compensation of the main model to reduce the prediction error. We conducted eight experiments of two different types of daily precipitation prediction problems using five metrics to evaluate our proposed model performance. Experimental results show that our model is comparable or even superior to five state-of-the-art models with high reliability in terms of all metrics on all datasets. It indicates that the proposed method is a promising alternative prediction tool for higher accuracy and credibility of regional daily precipitation prediction.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Yuzhong Peng AU - Huasheng Zhao AU - Hao Zhang AU - Wenwei Li AU - Xiao Qin AU - Jianping Liao AU - Zhiping Liu AU - Jie Li PY - 2019 DA - 2019/12/04 TI - An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction JO - International Journal of Computational Intelligence Systems SP - 1512 EP - 1525 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191126.001 DO - 10.2991/ijcis.d.191126.001 ID - Peng2019 ER -