Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

133
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
133
Survival Tree01:19

Survival Tree

87
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
87
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.8K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.8K
Precipitation Gravimetry01:03

Precipitation Gravimetry

6.6K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
6.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

133
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
133

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Detection of climate change signals using precipitation and temperature time series by a hybrid deep learning framework.

Environmental monitoring and assessment·2025
Same author

Advances in artificial intelligence to model the impact of El Niño-Southern Oscillation on crop yield variability.

MethodsX·2025
Same author

Sustainable management of coffee berry disease and leaf rust co-infection: a systematic review of deterministic models.

MethodsX·2025
Same author

Advancements in daily precipitation forecasting: A deep dive into daily precipitation forecasting hybrid methods in the Tropical Climate of Thailand.

MethodsX·2024
Same author

Long-Term Follow-Up of Cerebral Aneurysms Completely Occluded at 6 Months After Intervention with the Woven EndoBridge (WEB) Device: a Retrospective Multicenter Observational Study.

Translational stroke research·2023
Same author

Transcriptional Analysis of TP53 Gene in Chronic Hepatitis C Patients Treated with Sofosbuvir, Daclatasvir, Pegylated Interferon, and Ribavirin.

ACS omega·2023

相关实验视频

Updated: Jul 9, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.6K

缺少的每日降雨数据的归算;人工智能和统计技术之间的比较.

Angkool Wangwongchai1, Muhammad Waqas2,3, Porntip Dechpichai1

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

MethodsX
|November 29, 2023
PubMed
概括

这项研究评估了统计和人工智能技术,用于归因泰国缺少的每日降雨数据. 在山区推多重线性回归 (MLR),以确保其准确性和透明度.

关键词:
计量AIT缺少的每日降雨数据人工智能的人工智能是人工智能.深度学习是一种深度学习.计入计算是指计入计算的方法.机器学习 机器学习缺少的数据数据.神经网络的神经网络的神经网络降雨量 降雨量 降雨量

更多相关视频

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

相关实验视频

Last Updated: Jul 9, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.6K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

科学领域:

  • 水文学的水文学
  • 数据科学数据科学数据科学
  • 环境建模环境建模

背景情况:

  • 缺失的数据是水文建模中的一个重大挑战.
  • 准确计算每日降雨量对于可靠的水资源管理至关重要,特别是在复杂的地形中.

研究的目的:

  • 评估统计技术 (ST) 和人工智能技术 (AIT) 用于归因缺失的每日降雨数据.
  • 为泰国北部山区推一种有效的归算方法.

主要方法:

  • 从泰国北部的20个站点收集了30年的每日降雨数据.
  • 根据空间相关性,删除了四个站的25-35%数据.
  • 开发并评估了使用平均绝对误差 (MAE),根平均平方误差 (RMSE),R2和相关系数 (r) 的归算模型.
  • 与AITs (LSTM-RNN,M5模型树,MLPNN,SVR) 进行了ST (算术平均,多重线性回归,正常比率,NIPALS,线性插值) 的比较.

主要成果:

  • 多重线性回归 (MLR) 模型表现出强的表现,平均MAE为0.98,RMSE为4.52,R2为79.6%.
  • M5模型树 (M5-MT) 的表现也很好,平均MAE为0.91,RMSE为4.52,R2为79.8%.
  • 由于其准确性,透明度和易用性的平衡,MLR被确定为推的方法.

结论:

  • 无论是MLR还是M5-MT都是有效的,用于归因缺失的每日降雨数据.
  • 由于其透明度和最低限度的先决条件,建议在泰国北部山区实践应用MLR.
  • 该研究为解决水文研究中缺少数据的问题提供了有价值的框架.