A deep learning perspective on meteorological droughts prediction in the Mun River Basin, Thailand

Author:

Humphries Usa Wannasingha1ORCID,Waqas Muhammad23ORCID,Hliang Phyo Thandar23,Dechpichai Porntip1,Wangwongchai Angkool1ORCID

Affiliation:

1. Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT) 1 , Bangkok 10140, Thailand

2. The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut’s University of Technology Thonburi (KMUTT) 2 , Bangkok 10140, Thailand

3. Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation 3 , Bangkok, Thailand

Abstract

Accurate drought prediction is crucial for enhancing resilience and managing water resources. Developing robust forecasting models and understanding the variables influencing their outcomes are essential. This study developed models that integrate wavelet transformation (WT) with advanced artificial intelligence (AI) models, increasing prediction accuracy. This study investigates the prediction of meteorological droughts using standalone bootstrapped random forest (BRF) and bi-directional long short-term memory (Bi-LSTM) models, compared to wavelet-decomposed hybrid models (WBRF, WBi-LSTM). These models were evaluated in the Mun River Basin, Thailand, utilizing monthly meteorological data (1993–2022) from the Thai Meteorological Department. The predictions were assessed using statistical metrics (R2, MAE, RMSE, and MAPE). For the Standardized Precipitation Index (SPI), the hybrid WBRF model consistently outperformed the standalone BRF across various metrics and timescales, demonstrating higher R2 (0.89–0.97 for SPI-3) and lower error metrics (MAE: 0.144–0.21 for SPI-6, RMSE: 0.2–0.3 for SPI-12). Similarly, the hybrid WBi-LSTM model outperformed the standalone Bi-LSTM in SPI predictions, exhibiting higher R2 (0.87–0.91 for SPI-3) and lower error metrics (MAE: 0.19–0.23 for SPI-6, RMSE: 0.27–0.81 for SPI-12) across all timescales. This trend was also observed for the China Z-index, Modified China Z-index, Hutchinson Drought Severity Index, and Rainfall Anomaly Index, where hybrid models achieved superior performance compared to standalone models. The WBi-LSTM model emerged as the preferred choice across different timespans. The integration of WT enhanced the predictive accuracy of hybrid models, making them effective tools for drought prediction.

Funder

King Mongkut’s University of Technology Thonburi

Thailand Science Research and Innovation

Publisher

AIP Publishing

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3