Skip to main content

ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Hydrosphere
Volume 12 - 2024 | doi: 10.3389/feart.2024.1445241
This article is part of the Research Topic Water and Ecological System: Response, Management, and Restoration-Volume II View all 4 articles

A Study on Groundwater Level Calculation Based on PCA-CIWOABP

Provisionally accepted
XiaoLie ZHANG XiaoLie ZHANG 1*Xiaoyi GUO Xiaoyi GUO 1Shuyu LIU Shuyu LIU 2Xiutang SHANG Xiutang SHANG 1Zhiheng XU Zhiheng XU 1Jiankun ZHAO Jiankun ZHAO 3
  • 1 North China University of Water Conservancy and Electric Power, Zhengzhou, China
  • 2 Hohai University, Nanjing, Jiangsu Province, China
  • 3 Guangdong Research Institute of Water Resources and Hydropower, Guangzhou, China

The final, formatted version of the article will be published soon.

    In order to explore the relationship between groundwater levels and hydro-meteorological factors in Fengnan District, accurate estimation of groundwater levels in the area was undertaken. Real data on groundwater levels, water consumption, and rainfall from 2018 to 2021 in various townships within Fengnan District were selected. Utilizing the Principal Component Analysis method, the main influencing factors were extracted from the hydrological data of each township. Subsequently, a groundwater level calculation model was established using the CIWOABP(Cubic map -Intelligent weight adjustment -Whale Optimization Algorithm -Back Propagation) neural network in combination with these factors. The results indicate that: (1) Principal Component Analysis extracted a total of 5 principal components from various hydrological data in Fengnan District, namely groundwater levels of monitoring wells #11 and #12, rainfall from rainfall station r1, and water consumption from Fengnan(FN) and Qianying(QY) towns. (2) The CIWOABP neural network was trained using 36 sets of actual measurement data and validated with 12 sets of simulated data. The mean absolute errors (MAE) for monitoring wells #11 and #12 were 0.19 and 0.23 respectively, and the mean squared errors (MSE) were 0.05 and 0.09 respectively. The model exhibited high computational accuracy and can be effectively employed to calculate actual groundwater levels. The research outcomes can provide theoretical and methodological insights for groundwater resource management in the North China Plain.

    Keywords: Groundwater level, Principal Component Analysis, Intelligent weight adjustment, Whale optimization algorithm, BP neutral network 1

    Received: 07 Jun 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 ZHANG, GUO, LIU, SHANG, XU and ZHAO. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: XiaoLie ZHANG, North China University of Water Conservancy and Electric Power, Zhengzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.