Deep learning convolutional neural network in rainfall–runoff modelling
نویسندگان
چکیده
منابع مشابه
Learning Document Image Features With SqueezeNet Convolutional Neural Network
The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...
متن کاملDeep Columnar Convolutional Neural Network
Recent developments in the field of deep learning have shown that convolutional networks with several layers can approach human level accuracy in tasks such as handwritten digit classification and object recognition. It is observed that the state-of-the-art performance is obtained from model ensembles, where several models are trained on the same data and their predictions probabilities are ave...
متن کاملAn Overview of Convolutional Neural Network Architectures for Deep Learning
Since AlexNet was developed and applied to the ImageNet classi cation competition in 2012 [1], the quantity of research on convolutional networks for deep learning applications has increased remarkably. In 2015, the top 5 classi cation error was reduced to 3.57%, with Microsoft's Residual Network [2]. The previous top 5 classi cation error was 6.67%, achieved by GoogLeNet [3]. In recent years, ...
متن کاملTree-CNN: A Deep Convolutional Neural Network for Lifelong Learning
In recent years, Convolutional Neural Networks (CNNs) have shown remarkable performance in many computer vision tasks such as object recognition and detection. However, complex training issues, such as “catastrophic forgetting” and hyper-parameter tuning, make incremental learning in CNNs a difficult challenge. In this paper, we propose a hierarchical deep neural network, with CNNs at multiple ...
متن کاملDeep Convolutional Neural Network for Image Deconvolution
Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal linear convolution model due to camera noise, saturation, image compression, to name a few. Instead of perfectly modeling outliers, which is rather challenging from a generative model perspective, we develop a deep convolutional neural network to capture the characteristi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Hydroinformatics
سال: 2020
ISSN: 1464-7141,1465-1734
DOI: 10.2166/hydro.2020.095