A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture
Author:
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering
Reference96 articles.
1. High-resolution smap satellite soil moisture product: Exploring the opportunities;Abbaszadeh;Bull. Am. Meteorol. Soc.,2021
2. Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale;Abowarda;Remote Sens. Environ.,2021
3. Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models;Achieng;Comput. Geosci.,2019
4. Machine learning to estimate surface soil moisture from remote sensing data;Adab;Water,2020
5. Estimating soil moisture using remote sensing data: A machine learning approach;Ahmad;Adv. Water Resour.,2010
Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Optimized Ensemble Deep Random Vector Functional Link with Nature Inspired Algorithm and Boruta Feature Selection: Multi-site Intelligent Model for Air Quality Index Forecasting;Process Safety and Environmental Protection;2024-09
2. Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning;Frontiers in Plant Science;2024-08-29
3. Short-term wind power forecasting using the hybrid model of multivariate variational mode decomposition (MVMD) and long short-term memory (LSTM) neural networks;Electrical Engineering;2024-08-26
4. XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring;Systems;2024-07-14
5. PM2.5 concentration forecasting: Development of integrated multivariate variational mode decomposition with kernel Ridge regression and weighted mean of vectors optimization;Atmospheric Pollution Research;2024-06
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