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相关概念视频

Precipitation Processes01:12

Precipitation Processes

332
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
332
Precipitation Gravimetry01:03

Precipitation Gravimetry

3.8K
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...
3.8K
Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

25.0K
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.
25.0K
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.6K
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...
1.6K
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

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Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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基于深度学习的框架,用于增强参考蒸发透气估计:评估准确性和预测策略.

Suman Saurabh Sarkar1, Jatin Bedi2, Sushma Jain2

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India. ssarkar_phd20@thapar.edu.

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概括
此摘要是机器生成的。

时间卷积网络 (TCN) 模型准确地预测每日参考蒸发透气 (ETo),表现优于LSTM和N-BEATS. 使用TCN的递归策略进一步提高了在数据稀缺环境中的灌规划.

关键词:
这是LSTM的LSTM.在N-BEATS中,我们可以使用N-BEATS.参考蒸发透气 (ETo) 是指TCN TCN 是一个数字.时间序列预测时间序列预测

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Measurement of Leaf Hydraulic Conductance and Stomatal Conductance and Their Responses to Irradiance and Dehydration Using the Evaporative Flux Method EFM
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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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科学领域:

  • 农业工程 农业工程
  • 数据科学数据科学数据科学
  • 水文学的水文学

背景情况:

  • 优化农业资源管理,包括作物产量和用水量,对于粮食安全至关重要.
  • 准确估计参考蒸发透气 (ETo) 对于精确的灌至关重要,但传统方法往往昂贵且数据密集.
  • 有限的气象数据对可靠的ETo预测和灌规划构成重大挑战.

研究的目的:

  • 评估深度学习序列模型 (LSTM,N-BEATS,TCN) 的性能,以预测每日ETo.
  • 为了比较标准与递归预测策略的有效性,使用TCN进行ETo估计.
  • 确定ETo时间序列预测的有效方法,特别是在数据稀缺的农业环境中.

主要方法:

  • 三种深度学习序列模型 - - 长期短期记忆 (LSTM),时间序列神经基础扩展分析 (N-BEATS) 和时间卷积网络 (TCN) - - 被评估为每日ETo预测.
  • 确定为表现最佳的TCN模型进一步用于评估两个预测策略:使用历史数据的标准预测和涉及预测气候数据的递归方法.
  • 模型性能通过使用诸如纳什-萨特克利夫效率 (NSE),Theil U2,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 等指标进行了定量评估.

主要成果:

  • 时间卷积网络 (TCN) 模型在预测每日ETo方面表现优于LSTM和N-BEATS.
  • 该TCN模型实现了高精度的NSE=0.99,Theil U2=0.005,RMSE=0.092,和MAE=0.048. 这样可以达到高精度.
  • 使用TCN的递归预测策略比标准方法产生了更准确的ETo值,这对于数据稀缺的灌规划是有益的.

结论:

  • 深度学习模型,特别是TCN,为预测ETo时间序列提供了一个高效和准确的替代方案.
  • TCN模型的性能及其递归策略的有效性为农业中精确的水资源管理提供了宝贵的工具,特别是当气象数据有限时.
  • 这项研究强调了先进的人工智能技术的潜力,通过更好的灌规划来提高农业效率和可持续性.