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相关概念视频

Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
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Global Climate Change01:50

Global Climate Change

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Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
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Overview
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Reinforcement01:23

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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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...
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Updated: Jan 14, 2026

Using Generative Art to Convey Past and Future Climate Transitions
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Using Generative Art to Convey Past and Future Climate Transitions

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可以解释的气候预测深度强化学习与转移学习.

Thulasi Bikku1, Ramadevi Chappala2, Angotu Nageswara Rao3

  • 1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India.

Environmental science and pollution research international
|October 23, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了可解释强化气候建模框架 (ERCMF),用于准确和透明的气候预测. ERCMF集成人工智能技术,为气候科学家和政策制定者提供可靠的,适应性预测.

关键词:
人工智能 (AI) 是一种人工智能.气候建模的气候模型.深度强化学习 (DRL) 是一种深度强化学习.转移学习 (TL) 是指转移学习.

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科学领域:

  • 气候科学 气候科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 气候研究人员使用智能模型来了解过去的气候记录,并预测未来的气候演变.
  • 气候预测模型使用基于物理定律的高性能计算和数学方程来模拟地球系统.
  • 现有的模型往往缺乏气候预测的准确性,灵活性和解释性.

研究的目的:

  • 提出一个新的框架,即可解释强化气候建模框架 (ERCMF),用于准确,灵活和可解释的气候预测.
  • 将可解释AI (XAI) 和深度强化学习 (DRL) 整合到一个统一的气候建模方法中.
  • 通过向科学家和政策制定者提供有价值的见解,增强气候预测.

主要方法:

  • 使用转移学习 (TL) 来通过多变量气候表示学习 (MV-CRL) 来从卫星和地理空间数据中提取特征.
  • 使用基于深度强化学习的气候政策网络 (CPN) 来学习最佳预测策略.
  • 结合可解释的AI (XAI) 使用沙普利增量解释 (SHAP) 和注意力机制来预测可解释性.

主要成果:

  • 实现了1.84的根平均平方误差 (RMSE),超过了现有方法 (RMSE>2.25).
  • 证明了高绩效,时间一致性得分 (TCS) 为0.91和政策趋同得分 (PCS) 为0.91.
  • 排除关键模块显著增加了RMSE (高达2.85),并减少了一致性,突出了框架的综合方法.

结论:

  • ERCMF提供可靠,透明和适应性的气候预测,适合短期和长期分析.
  • 该框架独特地集成了TL,DRL和XAI,用于实时,可解释的气候预测.
  • 对于气候科学家和政策制定者来说,ERCMF提供了宝贵的见解,推动了气候建模能力的发展.