A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network
Abstract
:1. Introduction
2. Materials
3. Methodology
- (1)
- Convolution process of one-dimensional convolution network: In a one-dimensional convolutional neural network, the convolution of the first layer can be regarded as an operational relationship between the weight vector and the input vector . The weight vector has a size of m, that is, the size of the convolution kernel is m. More specifically, is the haze period vector as output, and each of the elements is a haze concentration value at a time point. The convolution kernel of m size is used to convolve the sequence of each m length in the input vector to obtain the output of the first layer. Where , this ensures that the haze concentration value at each moment in the input is included in the convolution operation. If the step size is 1, the convolution formula is as follows:
- (2)
- One-dimensional convolution layer: Since the one-dimensional convolution input layer is a one-dimensional vector, its convolution kernel is also one-dimensional. To illustrate the convolution process more specifically, a one-dimensional convolution process with an input length of 7, a convolution kernel size of 5, and a convolution step of one is shown in Figure 2.
- (3)
- One-dimensional convolution pooling layer: Due to the existence of the pooling layer, the one-dimensional convolutional neural network also fully exerts the neural network in theory due to the feature extraction, which can be equivalent in theory. The training speed of one-dimensional convolutional neural networks is inherently superior to other neural networks.
4. Experiments and Results
4.1. Comparison of 1D Convolutional Neural Network and GRU Circulatory Neural Network
- The input of the front layer and the input of this unit are weighted and then passed through an activation function to obtain an alternative value.
- Then, the input of the same front layer and the input of this unit are subjected to the same weighting, and the sigmoid activation function is mapped to 0, 1. This mapping value is the update gating parameter.
- Determine whether the parameter is updated by a gate control unit. By changing the relationship between the hidden layers of the cyclic neural network, the problem of weak correlation before and after the sequence input is solved.
- (1)
- Convolution: The number of iterations required for the neural network to achieve the best accuracy is less than that of the cyclic neural network. The convolutional neural network stops after 1600 iterations, and the cyclic neural network only achieves the optimal result as specified by us at the 2200th iteration.
- (2)
- Due to the characteristics of weight sharing and the local connection of the convolutional neural network, the training speed of the convolutional neural network is faster. The time for the convolutional neural network is about 120 s per 100 iterations, while the cyclic neural network is every 100 iterations. The time required is about 600 s.
- (3)
- Overall, although the initial accuracy of GRU increases slower than the one-dimensional convolutional neural network, optimization results continue to appear as the rounds increase. After more rounds of training, the GRU recurrent neural network can also obtain better results.
4.2. Experimental Process of One-Dimensional Convolutional Neural Network
5. Discussion
6. Conclusions
- The connection between layers in a convolutional neural network is sparsely connected and convoluted using a convolution kernel that is much smaller than the size of the input data, resulting in a smaller feature vector. This not only reduces the number of parameters but also reduces the storage size of the model, and the requirements for the amount of calculation are greatly reduced. Therefore, the efficiency of the one-dimensional convolutional neural network is significantly improved, and the time complexity is also greatly reduced.
- Parameter sharing: This makes the convolutional neural network robust to the translation of the input data. We can obtain the characteristics of these sequences by a one-dimensional convolution calculation. If we shift this feature event in the input, that is, delay it backward for a period of time, the convolution will still have exactly the same output value, but with the time delayed. This feature is beneficial for the extraction of time-dimensional features by one-dimensional convolutional neural networks and improves the accuracy of the network’s fitting of nonlinear mapping between input and output.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Level | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Concentration | 35 | 70 | 105 | 140 | 175 | 210 | 245 | 280 | 315 | 500 |
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Zhang, Z.; Tian, J.; Huang, W.; Yin, L.; Zheng, W.; Liu, S. A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network. Atmosphere 2021, 12, 1327. https://doi.org/10.3390/atmos12101327
Zhang Z, Tian J, Huang W, Yin L, Zheng W, Liu S. A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network. Atmosphere. 2021; 12(10):1327. https://doi.org/10.3390/atmos12101327
Chicago/Turabian StyleZhang, Ziyan, Jiawei Tian, Weizheng Huang, Lirong Yin, Wenfeng Zheng, and Shan Liu. 2021. "A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network" Atmosphere 12, no. 10: 1327. https://doi.org/10.3390/atmos12101327