半干旱气候区气象干旱预报的机器学习模型

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES
Chaitanya Baliram Pande, Dinesh Kumar Vishwakarma, Aman Srivastava, Kanak N. Moharir, Fahad Alshehri, Norashidah Md Din, Lariyah Mohd Sidek, Bojan Đurin, Abebe Debele Tolche
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引用次数: 0

摘要

印度马哈拉施特拉邦中部地区易受农业、气象和水文干旱影响,影响当地生态系统。历史数据的缺乏阻碍了对区域干旱的监测和预测。鉴于集成和机器学习(ML)模型用于干旱预测的研究有限,本研究比较了五种ML模型[鲁棒线性回归,袋装树,提升树,支持向量机(SVM)和母高斯过程回归(GPR)],以确定在区域背景下的优越准确性。该研究旨在评估开发的模型在预测未来干旱事件方面的准确性,并利用标准化降水指数(SPI)-SPI-3和SPI-6深入了解热带气候的气象干旱。子集回归分析显示,SPI-1、-3、-4、-5和-6是SPI-3的最佳输入子集,而SPI-1和-2是SPI-6的最佳输入子集。结果表明,在SPI-3和SPI-6训练阶段,Matern GPR模型优于其他模型(MSE = 0.1954, 0.0493;Rmse = 0.4420, 0.2221;Mae = 0.3382, 0.1683;Mare = 1.3807, 0.5237;Nse = 0.6585, 0.9048;R = 0.9165, 0.9920;R2 = 0.8399, 0.9841)。在检验中,SVM模型对SPI-3和SPI-6的预测效果较好(MSE = 0.5735, 0.8479;Rmse = 0.7573, 0.9208;Mae = 0.5882, 0.5300;Mare =−0.5638,−0.3621;Nse = 0.8676, 0.8601;R = 0.9317, 0.9275;R2 = 0.8680, 0.8603)。集成方法通过开发基于各种算法的ML模型,在显着提高干旱预测的准确性方面发挥了新颖而关键的作用,这些模型比精确模型运行更高效,需要更少的输入,并且表现出更低的复杂性,证明了干旱预警系统的有效性。因此,研究结果为作物规划、干旱挑战、水资源管理和维持研究区生态系统提供了有价值的见解。总之,该研究通过采用先进的数据输入技术、集成学习方法以及结合SVM和Matern GPR等鲁棒ML模型,解决了以往数据不完整对区域干旱事件监测和预测带来的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel machine learning models for meteorological drought forecasting in the semi-arid climate  region

The central region of Maharashtra, India, is susceptible to agriculture, meteorological, and hydrological droughts, impacting local ecosystems. The scarcity of historical data  impedes monitoring and forecasting regional droughts. Given the limited studies on ensemble and Machine Learning (ML) models for drought forecasting, this research compares five ML models [Robust Linear Regression, Bagged Trees, Boosted Trees, Support Vector Machine (SVM), and Matern Gaussian Process Regression (GPR)] to determine superior accuracy in the regional context. The study aims to assess the accuracy of the developed models in predicting future drought events and gain insights into meteorological droughts in tropical climates using the Standardized Precipitation Index (SPI)-SPI-3 and SPI-6. Subset regression analysis exhibited SPI-1, -3, -4, -5, and -6 as the best input subsets for SPI-3, whereas SPI-1 and -2 for SPI-6. Results indicated that the Matern GPR model outperformed other models in SPI-3 and SPI-6 training phases (MSE = 0.1954, 0.0493; RMSE = 0.4420, 0.2221; MAE = 0.3382, 0.1683; MARE = 1.3807, 0.5237; NSE = 0.6585, 0.9048; R = 0.9165, 0.9920; R2 = 0.8399, 0.9841). In testing, the SVM model bettered  in SPI-3 and SPI-6 forecasting (MSE = 0.5735, 0.8479; RMSE = 0.7573, 0.9208; MAE = 0.5882, 0.5300; MARE = − 0.5638, − 0.3621; NSE = 0.8676, 0.8601; R = 0.9317, 0.9275; R2 = 0.8680, 0.8603). The ensemble method played a novel and crucial role in significantly improving the accuracy of drought forecasting by developing ML models based on various algorithms that operate more efficiently, require fewer inputs, and exhibit less complexity than precise models, proving highly effective for drought warning systems. Therefore, results offer valuable insights for crop planning, drought challenges, water management, and maintaining the study area ecosystem. In conclusion, the study addressed the challenge proposed  by incomplete previous data for monitoring and forecasting regional drought events by employing advanced data imputation techniques, ensemble learning methods, and incorporating robust ML  models like SVM and Matern GPR.

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来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
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