通过使用各种人工智能技术和传统的回归计算,对每日蒸发转化估计的比较研究
Hasan Güzel1, Fatih Üneş1, Merve Erginer1
1Department of Civil Engineering, Iskenderun Technical University, Turkey.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
精确的蒸发透气估计对于水结构设计至关重要. 人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 模型在预测日常蒸发转移方面表现优异,与其他方法相比.
科学领域:
- 水文和水资源工程 水文和水资源工程
- 环境科学 环境科学
- 计算智能是一种计算智能.
背景情况:
- 蒸发透气 (ET) 是一个关键的水文参数,影响水资源管理和基础设施设计.
- 精确的ET估计对于优化水结构效率和确保设计安全至关重要.
- 影响ET的因素包括温度,湿度,风速和太阳辐射.
研究的目的:
- 开发和比较各种模型来估计每日蒸发透气量 (ET).
- 评估机器学习技术的性能与ET预测的传统方法相比.
- 为水文应用确定最准确的模型.
主要方法:
- 开发了使用模糊规则生成技术 (fuzzy-SMRGT),多变量回归 (MR),人工神经网络 (ANN),自适应神经模糊推理系统 (ANFIS) 和支持向量回归 (SMOReg) 的模型.
- 利用来自德克萨斯州易斯维尔湖的空气温度,风速,太阳辐射和相对湿度的每日数据.
- 使用Penman-Monteith (PM) 方法作为经验ET计算的参考方程.
主要成果:
- 人工神经网络 (ANN) 获得了最高的精度,R2=0.998,RMSE=0.075和APE=3.361%.
- 适应性神经模糊推理系统 (ANFIS) 也表现出很好的表现,R2=0.996,RMSE=0.103,APE=4.340%.
- 四次多变量回归 (Q-MR) 的表现很好,优于其他回归和支向量方法.
结论:
- ANN和ANFIS模型提供非常准确的每日蒸发透气估计.
- 这些先进的计算模型比传统的水文研究方法有了显著的改进.
- 使用ANN和ANFIS准确的ET预测可以导致更高效,更安全的水资源管理.
相关概念视频
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...
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
Regression Analysis
5.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.8K
Regulation of Transpiration by Stomata
28.5K
During photosynthesis, plants acquire the necessary carbon dioxide and release the produced oxygen back into the atmosphere. Openings in the epidermis of plant leaves is the site of this exchange of gasses. A single opening is called a stoma—derived from the Greek word for “mouth.” Stomata open and close in response to a variety of environmental cues.
28.5K
Adaptations that Reduce Water Loss
25.8K
Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
25.8K
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Survival Tree
118
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...
Building a Survival Tree
Constructing a...
118


