In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six dist...In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed,India.These models included the artificial neural network(ANN),k-nearest neighbour regression model(KNN),extreme gradient boosting(XGBoost)regression model,random forest regression model(RF),convolutional neural network(CNN),and CNN-RNN(convolutional recurrent neural network).The years 2003-2007 are classified as the calibration or training period,while the years 2008-2009 are classified as the validation or testing period for the span of time 2003 to 2009.The available rainfall,maximum and minimum temperatures,and discharge data were collected and utilized in the models.To compare the performance of the models,five criteria were employed:R^(2),NSE,MAE,RMSE,and PBIAS.The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods(training:R^(2) is 0.99,NSE is 0.99,MAE is 1.76,RMSE is 3.11,and PBIAS is1.45;testing:R^(2) is 0.97,NSE is 0.97,MAE is 2.05,RMSE is 3.60,and PBIAS is3.94).These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study.The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management,flood control,and environmental planning.展开更多
针对一些缺少参考对齐的本体匹配任务,提出一种基于深度无监督学习的匹配技术,通过对文本的上下文信息进行学习,提取到抽象文本特征,以此找到对齐。由于高维度输入会影响计算的效率,针对本体的多种描述构建CNN(convolutional neural net...针对一些缺少参考对齐的本体匹配任务,提出一种基于深度无监督学习的匹配技术,通过对文本的上下文信息进行学习,提取到抽象文本特征,以此找到对齐。由于高维度输入会影响计算的效率,针对本体的多种描述构建CNN(convolutional neural network)模块并且和不同的RNN(recurrent neural network)串行连接实现特征降维,提出一种改进的基于BiLSTM(bidirectional long and short term memory neural network)的注意力机制来提取较好的抽象特征。提出一种多主导的对齐集成策略将本体不同层次的对齐进行合并,提高匹配的质量。实验在OAEI(ontology alignment evaluation initiative)的benchmark测试集上进行,提出方法的评价指标较高,并且和其它匹配系统作比较,高质量的对齐验证了所提方法具有一定的先进性和创新性。展开更多
随着高通量测序技术的迅猛发展,基因组学领域迎来了数据量的爆炸性增长,这对传统生物信息学处理复杂数据模式的能力构成了严峻挑战。在此技术革新的关键时刻,深度学习作为人工智能领域的前沿技术,以其强大的数据解析与模式识别能力,为...随着高通量测序技术的迅猛发展,基因组学领域迎来了数据量的爆炸性增长,这对传统生物信息学处理复杂数据模式的能力构成了严峻挑战。在此技术革新的关键时刻,深度学习作为人工智能领域的前沿技术,以其强大的数据解析与模式识别能力,为基因组学研究注入了新的活力。本文聚焦于4种核心深度学习模型——卷积神经网络(convolution neural network,CNN)、循环神经网络(recurrent neural network,RNN)、长短期记忆网络(long short term memory,LSTM)及生成对抗网络(generative adversarial network,GAN),系统阐述了它们的基础原理,重点回顾了这些模型近5年在DNA、RNA和蛋白质研究领域的广泛应用。此外,文章进一步探讨了深度学习在畜禽基因组学中的应用案例,揭示了其在遗传特征解析、疾病预防以及遗传改良等领域的潜在应用价值与面临的挑战。通过深入分析,本文旨在阐述深度学习技术在增强基因组数据分析的准确性和处理能力方面的作用,并构建一个概念性框架,以指导畜禽基因组学研究策略的发展及其在具体场景下的应用,进而推动精准农业和遗传改良技术的发展。展开更多
文摘In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed,India.These models included the artificial neural network(ANN),k-nearest neighbour regression model(KNN),extreme gradient boosting(XGBoost)regression model,random forest regression model(RF),convolutional neural network(CNN),and CNN-RNN(convolutional recurrent neural network).The years 2003-2007 are classified as the calibration or training period,while the years 2008-2009 are classified as the validation or testing period for the span of time 2003 to 2009.The available rainfall,maximum and minimum temperatures,and discharge data were collected and utilized in the models.To compare the performance of the models,five criteria were employed:R^(2),NSE,MAE,RMSE,and PBIAS.The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods(training:R^(2) is 0.99,NSE is 0.99,MAE is 1.76,RMSE is 3.11,and PBIAS is1.45;testing:R^(2) is 0.97,NSE is 0.97,MAE is 2.05,RMSE is 3.60,and PBIAS is3.94).These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study.The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management,flood control,and environmental planning.
文摘针对一些缺少参考对齐的本体匹配任务,提出一种基于深度无监督学习的匹配技术,通过对文本的上下文信息进行学习,提取到抽象文本特征,以此找到对齐。由于高维度输入会影响计算的效率,针对本体的多种描述构建CNN(convolutional neural network)模块并且和不同的RNN(recurrent neural network)串行连接实现特征降维,提出一种改进的基于BiLSTM(bidirectional long and short term memory neural network)的注意力机制来提取较好的抽象特征。提出一种多主导的对齐集成策略将本体不同层次的对齐进行合并,提高匹配的质量。实验在OAEI(ontology alignment evaluation initiative)的benchmark测试集上进行,提出方法的评价指标较高,并且和其它匹配系统作比较,高质量的对齐验证了所提方法具有一定的先进性和创新性。
文摘随着高通量测序技术的迅猛发展,基因组学领域迎来了数据量的爆炸性增长,这对传统生物信息学处理复杂数据模式的能力构成了严峻挑战。在此技术革新的关键时刻,深度学习作为人工智能领域的前沿技术,以其强大的数据解析与模式识别能力,为基因组学研究注入了新的活力。本文聚焦于4种核心深度学习模型——卷积神经网络(convolution neural network,CNN)、循环神经网络(recurrent neural network,RNN)、长短期记忆网络(long short term memory,LSTM)及生成对抗网络(generative adversarial network,GAN),系统阐述了它们的基础原理,重点回顾了这些模型近5年在DNA、RNA和蛋白质研究领域的广泛应用。此外,文章进一步探讨了深度学习在畜禽基因组学中的应用案例,揭示了其在遗传特征解析、疾病预防以及遗传改良等领域的潜在应用价值与面临的挑战。通过深入分析,本文旨在阐述深度学习技术在增强基因组数据分析的准确性和处理能力方面的作用,并构建一个概念性框架,以指导畜禽基因组学研究策略的发展及其在具体场景下的应用,进而推动精准农业和遗传改良技术的发展。
