Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean
Abstract
:1. Introduction
2. Data and Methods
2.1. Improved DNN Model
2.2. BGC-Argo Data for the IDNN Model
2.3. Satellite Data for the IDNN Model
2.4. Training Process
3. Results and Discussion
3.1. IDNN-Retrieved Vertical Chl a Profiles
3.2. IDNN-Retrieved SCM Characteristics
3.3. Role of the Gaussian Activation Function in Enhancing Estimation Accuracy
- (i)
- DNN model using a sigmoid activation function with bias improvement by incorporating SCM depth (Equation (1)) (hereafter, referred to as DNN-b);
- (ii)
- DNN model using random bias values with a Gaussian activation function, rather than a sigmoid function (Equation (4)) (hereafter, referred to as DNN-G).
- (iii)
- DNN-G model in which the bias term b was set as 0 (Equation (5)) (hereafter, referred to as DNN-G-b0),
- (iv)
- DNN-G model in which the bias term b was set as 1 (Equation (6)) (hereafter, referred to as DNN-G-b1),
3.4. Comparison with Shallow ANNs
3.5. Application of the IDNN Model to Satellite Data
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Determination Coefficient | |
Pearson’s Correlation Coefficient | |
Root Mean Square Error | |
Mean Absolute Percentage Error | |
Mean Bias Error | |
Mean Relative Bias Error |
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Region | Float No. (Number of Profiles) | Data Duration | SCM Intensity (mg m−3) | SCM Depth (m) |
---|---|---|---|---|
BOX1 (12–24°N, 123–156°E) | 2902753 (118) | 2019/3/30–2019/12/8 | 0.57 (0.37–0.85) | 117 (88–166) |
2902756 (184) | 2019/3/25–2020/12/2 | 0.67 (0.34–0.73) | 115 (88–150) | |
2902762 (82) | 2020/8/16–2021/4/18 | 0.43 (0.29–0.77) | 139 (90–175) | |
2902822 (37) | 2021/1/12–2021/4/17 | 0.43 (0.29–0.53) | 127 (97–149) | |
2902823 (30) | 2021/1/17–2021/4/17 | 0.41 (0.19–0.55) | 147 (129–183) | |
2902824 (30) | 2021/1/20–2021/4/16 | 0.47 (0.39–0.55) | 153 (127–176) | |
Seasonal average (Winter, Spring, Summer, Autumn) | 0.44, 0.59, 0.58, 0.57 | 134, 124, 109, 128 | ||
BOX2 (26–38°N, 132–173°E) | 2902748 (199) | 2018/5/31–2021/4/17 | 1.23 (0.55–3.54) | 76 (30–117) |
2902749 (28) | 2018/5/31–2018/9/8 | 1.07 (0.61–2.18) | 79 (40–108) | |
2902750 (108) | 2018/9/13–2019/5/31 | 0.85 (0.44–1.67) | 89 (29–118) | |
2902754 (147) | 2018/8/30–2021/4/16 | 1.1 (0.32–6.65) | 76 (23–135) | |
2902755 (9) | 2019/10/19–2019/11/28 | 0.65 (0.58–1.17) | 40 (18–62) | |
2903213 (1) | 2018/2/22–2018/2/22 | 0.91 (0.91–0.91) | 68 (68–68) | |
2903394 (78) | 2019/5/26–2020/12/8 | 0.79 (0.45–6.12) | 65 (28–100) | |
Seasonal average (Winter, Spring, Summer, Autumn) | 0.62, 1.18, 1.49, 0.91 | 74, 69, 74, 83 | ||
BOX3 (38–48°N, 145–180°E) | 2902755 (204) | 2018/9/3–2021/4/16 | 1.92 (0.50–5.70) | 41 (12–96) |
2903354 (87) | 2018/7/25–2019/9/4 | 2.5 (0.24–7.07) | 28 (6–49) | |
Seasonal average (Winter, Spring, Summer, Autumn) | 1.09, 1.60, 2.40, 2.00 | 59, 39, 33, 40 | ||
Total area | (1342) | 2018/2/22–2021/4/18 | 1.14 (0.20–7.07) | 85 (6–183) |
Seasonal average (Winter, Spring, Summer, Autumn) | 0.52, 0.88, 1.57, 1.17 | 115, 101, 72, 75 |
Network Parameter | Parameters | ||
---|---|---|---|
BOX1 | BOX2 | BOX3 | |
Hidden layer depth | 3 | 3 | 4 |
Number of hidden neurons | 64, 64, 64 | 64, 64, 64 | 64, 128, 128, 64 |
Momentum | 0.9 | 0.9 | 0.9 |
Epoch | 115 | 115 | 150 |
Learning rate | 0.01 | 0.01 | 0.01 |
Dropout rate | 0.1 | 0.1 | 0.1 |
Index | Region | ||
---|---|---|---|
BOX1 | BOX2 | BOX3 | |
R2 | 0.77 | 0.72 | 0.71 |
𝜌 | 0.89 | 0.88 | 0.87 |
RMSE | 0.0040 | 0.025 | 0.11 |
MAPE | 0.036 | 0.073 | 0.13 |
Region | Season | SCM Depth (m) | SCM Intensity (mg m−3) | SCM Thickness (m) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Obs. | Model | MAPE | Obs. | Model | MAPE | Obs. | Model | MAPE | ||
BOX1 | Winter | 136 | 136 | 9% | 0.45 | 0.37 | 20% | 70 | 73 | 21% |
Spring | 126 | 126 | 10% | 0.59 | 0.52 | 19% | 75 | 85 | 25% | |
Summer | 120 | 125 | 19% | 0.62 | 0.50 | 21% | 75 | 78 | 31% | |
Autumn | 110 | 115 | 8% | 0.58 | 0.47 | 19% | 75 | 80 | 15% | |
BOX2 | Winter | 76 | 75 | 14% | 0.62 | 0.54 | 21% | 80 | 76 | 21% |
Spring | 69 | 73 | 25% | 1.04 | 0.78 | 21% | 65 | 70 | 32% | |
Summer | 71 | 75 | 16% | 1.13 | 1.01 | 28% | 43 | 56 | 22% | |
Autumn | 80 | 82 | 12% | 0.94 | 0.65 | 27% | 54 | 64 | 26% | |
BOX3 | Spring | 40 | 35 | 35% | 1.80 | 1.70 | 30% | 69 | 56 | 20% |
Summer | 34 | 32 | 21% | 2.48 | 2.49 | 29% | 43 | 42 | 31% | |
Autumn | 42 | 44 | 24% | 2.12 | 1.80 | 15% | 39 | 41 | 22% |
Index | Region | ||
---|---|---|---|
BOX1 | BOX2 | BOX3 | |
R2 | 0.79 | 0.66 | 0.47 |
𝜌 | 0.91 | 0.86 | 0.89 |
RMSE | 0.0046 | 0.026 | 0.14 |
MAPE | 0.037 | 0.076 | 0.15 |
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Chen, J.; Gong, X.; Guo, X.; Xing, X.; Lu, K.; Gao, H.; Gong, X. Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean. Remote Sens. 2022, 14, 632. https://doi.org/10.3390/rs14030632
Chen J, Gong X, Guo X, Xing X, Lu K, Gao H, Gong X. Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean. Remote Sensing. 2022; 14(3):632. https://doi.org/10.3390/rs14030632
Chicago/Turabian StyleChen, Jianqiang, Xun Gong, Xinyu Guo, Xiaogang Xing, Keyu Lu, Huiwang Gao, and Xiang Gong. 2022. "Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean" Remote Sensing 14, no. 3: 632. https://doi.org/10.3390/rs14030632