Predicting Monthly Runoff Interval by Using CNN-BiLSTM Optimized by Crested Porcupine Optimizer and Kernel Density Estimation

[Objective] To address the low accuracy of monthly runoff point prediction and the difficulty in describing the uncertainty of point prediction results, this study proposes a monthly runoff point prediction model and an interval prediction model based on the Crested Porcupine Optimizer (CPO), Convol...

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Bibliographic Details

Published in:	长江科学院院报
Main Author:	WU Xiao-tao, GUO Xin, YUAN Xiao-hui, YAN Li-juan, ZENG Zhi-qiang, LU Tao
Format:	Article
Language:	Chinese
Published:	Editorial Office of Journal of Changjiang River Scientific Research Institute 2025-09-01
Subjects:	monthly runoff prediction|crested porcupine optimizer|convolutional neural network|bidirectional long short-term memory network|nonparametric kernel density estimation
Online Access:	http://ckyyb.crsri.cn/fileup/1001-5485/PDF/1735798885288-1427253519.pdf