Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site and Data Sources
2.2. IGA–BP Neural Network Prediction Model of Soil Nutrient
2.2.1. Determination of Number of Neurons
2.2.2. Encoding Scheme
2.2.3. Adaptation Function
2.2.4. Crossover and Variation Operators
Algorithm 1 The improved GA |
Input: : Training set : Validation set : Maximum number of generations Initialize: G = 1; : the initial population randomly in accordance with the structure of the BP neural network; : Set of chromosomes with the largest fitness value in each generation; and : Fitness value of each generation’s best chromosome on training set and validation set. Begin 1. 2. , where denotes the n′-th chromosome of P, and 3. . Repeat for i = 1 to N do 4. Select and in accordance with the roulette wheel strategy. 5. Implement the selective and mutation operations proposed in this study. 6. Calculate if , then 7. Implement the replacement operation end if end for 8. Repeat 2. 9. Repeat 3. g = g + 1 Until g > G End Output: The best chromosome BC[], where = FV. |
2.3. Soil Nutrient Time Series Prediction Process
- (1)
- Soil composition data related to the predicted soil nutrients were obtained and preprocessed. Soil samples were divided into training and validation sets.
- (2)
- A BP neural network model was constructed.
- (3)
- The IGA algorithm was used to determine the weights and thresholds of the BP neural network.
- (4)
- The BP neural network was trained in accordance with the optimal weights and thresholds, and the IGA–BP neural network model was used.
- (5)
- Soil composition data were inputted into the IGA–BP neural network model. After that, soil nutrients were predicted.
3. Results
3.1. Weights and Thresholds of IGA Initialization BP Neural Network
3.2. Time Series Prediction of pH Value in Soil
3.3. Time Series Prediction of Total Nitrogen Value in Soil
3.4. Time Series Prediction of Organic Matter Value in Soil
3.5. Time Series Prediction of Fast-Acting Potassium Value in Soil
3.6. Time Series Prediction of Available Phosphorus Value in Soil
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Township Name | Zhufeng Street | Gangkou Town | Jialu Town | Wangxi Street | Xiaxi Town | Zhongxi Town |
---|---|---|---|---|---|---|
Latitude (°) | 30.5651 | 30.6902 | 30.4329 | 30.6902 | 30.5064 | 30.4942 |
Longitude (°) | 118.9462 | 118.9896 | 118.8619 | 118.9896 | 118.9501 | 119.1702 |
Soil Nutrients | Township Name | Year 2014 | Year 2015 | ||||||
---|---|---|---|---|---|---|---|---|---|
Actual Value | IGA–BP | GA–BP | BP | Actual Value | IGA–BP | GA–BP | BP | ||
pH value | Zhufeng Street | 6.50 | 6.41 | 6.26 | 6.73 | 6.30 | 6.42 | 5.82 | 5.87 |
Gangkou Town | 6.25 | 6.18 | 5.86 | 5.81 | 6.65 | 6.55 | 6.59 | 6.31 | |
Jialu Town | 5.60 | 5.67 | 5.27 | 5.85 | 5.85 | 5.72 | 5.78 | 5.73 | |
Wangxi Street | 5.70 | 5.97 | 5.70 | 5.31 | 6.30 | 6.07 | 5.87 | 6.22 | |
Xiaxi Town | 6.20 | 6.22 | 6.12 | 5.77 | 6.40 | 6.38 | 6.34 | 6.01 | |
Zhongxi Town | 6.06 | 6.26 | 5.92 | 5.75 | 6.27 | 6.06 | 6.00 | 5.99 | |
MSE | 0.022 | 0.056 | 0.123 | MSE | 0.023 | 0.084 | 0.092 | ||
RMSE | 0.150 | 0.237 | 0.351 | RMSE | 0.151 | 0.290 | 0.303 |
Soil Nutrients | Township Name | Year 2014 | Year 2015 | ||||||
---|---|---|---|---|---|---|---|---|---|
Actual Value | IGA–BP | GA–BP | BP | Actual Value | IGA–BP | GA–BP | BP | ||
Total nitrogen (%) | Zhufeng Street | 1.730 | 1.734 | 1.825 | 1.739 | 1.720 | 1.724 | 1.768 | 1.658 |
Gangkou Town | 1.700 | 1.665 | 1.751 | 1.832 | 1.780 | 1.810 | 1.768 | 1.757 | |
Jialu Town | 1.640 | 1.634 | 1.618 | 1.639 | 1.035 | 0.992 | 1.067 | 1.342 | |
Wangxi Street | 1.510 | 1.499 | 1.504 | 1.626 | 1.440 | 1.406 | 1.474 | 1.489 | |
Xiaxi Town | 1.370 | 1.322 | 1.443 | 1.471 | 1.497 | 1.534 | 1.584 | 1.483 | |
Zhongxi Town | 1.546 | 1.527 | 1.633 | 1.582 | 1.303 | 1.333 | 1.366 | 1.342 | |
MSE | 0.0007 | 0.0042 | 0.0071 | MSE | 0.0010 | 0.0027 | 0.0171 | ||
RMSE | 0.0263 | 0.0646 | 0.0841 | RMSE | 0.0321 | 0.0519 | 0.1306 |
Soil Nutrients | Township Name | Year 2014 | Year 2015 | ||||||
---|---|---|---|---|---|---|---|---|---|
Actual Value | IGA–BP | GA–BP | BP | Actual Value | IGA–BP | GA–BP | BP | ||
Organic matter (g/kg) | Zhufeng Street | 34.20 | 33.20 | 34.44 | 34.04 | 34.16 | 33.16 | 34.98 | 33.83 |
Gangkou Town | 33.28 | 34.04 | 34.47 | 31.30 | 35.57 | 34.92 | 36.31 | 33.44 | |
Jialu Town | 31.85 | 31.81 | 32.71 | 29.91 | 20.86 | 21.00 | 20.29 | 19.20 | |
Wangxi Street | 29.40 | 30.70 | 28.92 | 26.93 | 29.80 | 29.62 | 28.82 | 27.27 | |
Xiaxi Town | 26.70 | 26.85 | 27.94 | 24.91 | 29.87 | 29.00 | 29.17 | 27.78 | |
Zhongxi Town | 30.24 | 30.74 | 31.56 | 30.21 | 26.04 | 27.07 | 27.62 | 25.87 | |
MSE | 0.592 | 0.956 | 1.393 | MSE | 0.549 | 0.915 | 1.485 | ||
RMSE | 0.769 | 0.978 | 1.180 | RMSE | 0.741 | 0.956 | 1.218 |
Soil Nutrients | Township Name | Year 2014 | Year 2015 | ||||||
---|---|---|---|---|---|---|---|---|---|
Actual Value | IGA–BP | GA–BP | BP | Actual Value | IGA–BP | GA–BP | BP | ||
Fast-acting potassium (mg/kg) | Zhufeng Street | 99.00 | 98.61 | 97.63 | 94.63 | 175.94 | 175.02 | 176.08 | 179.96 |
Gangkou Town | 81.25 | 80.91 | 80.46 | 77.47 | 114.36 | 114.00 | 113.88 | 116.86 | |
Jialu Town | 123.50 | 124.38 | 125.39 | 127.94 | 179.20 | 180.17 | 180.69 | 178.44 | |
Wangxi Street | 62.00 | 61.26 | 63.65 | 62.86 | 43.10 | 43.50 | 44.73 | 45.82 | |
Xiaxi Town | 125.00 | 124.30 | 124.77 | 123.12 | 200.48 | 200.57 | 199.55 | 196.80 | |
Zhongxi Town | 79.40 | 80.27 | 77.61 | 82.55 | 67.11 | 67.71 | 66.76 | 63.02 | |
MSE | 0.474 | 1.999 | 11.229 | MSE | 0.409 | 1.017 | 10.110 | ||
RMSE | 0.688 | 1.414 | 3.351 | RMSE | 0.640 | 1.008 | 3.179 |
Soil Nutrients | Township Name | Year 2014 | Year 2015 | ||||||
---|---|---|---|---|---|---|---|---|---|
Actual Value | IGA–BP | GA–BP | BP | Actual Value | IGA–BP | GA–BP | BP | ||
Available phosphorus (mg/kg) | Zhufeng Street | 12.60 | 13.38 | 12.64 | 13.46 | 17.20 | 17.45 | 17.57 | 16.99 |
Gangkou Town | 5.53 | 5.20 | 6.28 | 5.85 | 7.55 | 8.24 | 8.00 | 8.25 | |
Jialu Town | 2.40 | 2.43 | 2.41 | 3.27 | 6.50 | 6.93 | 6.22 | 7.29 | |
Wangxi Street | 19.60 | 19.61 | 19.21 | 19.76 | 14.20 | 13.78 | 13.65 | 15.15 | |
Xiaxi Town | 29.70 | 29.65 | 30.52 | 30.48 | 8.60 | 8.60 | 9.17 | 9.45 | |
Zhongxi Town | 16.36 | 16.28 | 17.13 | 14.79 | 7.17 | 6.96 | 6.57 | 7.82 | |
MSE | 0.122 | 0.331 | 0.783 | MSE | 0.159 | 0.233 | 0.536 | ||
RMSE | 0.349 | 0.576 | 0.885 | RMSE | 0.398 | 0.483 | 0.732 |
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Liu, Y.; Jiang, C.; Lu, C.; Wang, Z.; Che, W. Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks. Symmetry 2023, 15, 151. https://doi.org/10.3390/sym15010151
Liu Y, Jiang C, Lu C, Wang Z, Che W. Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks. Symmetry. 2023; 15(1):151. https://doi.org/10.3390/sym15010151
Chicago/Turabian StyleLiu, Yanqing, Cuiqing Jiang, Cuiping Lu, Zhao Wang, and Wanliu Che. 2023. "Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks" Symmetry 15, no. 1: 151. https://doi.org/10.3390/sym15010151
APA StyleLiu, Y., Jiang, C., Lu, C., Wang, Z., & Che, W. (2023). Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks. Symmetry, 15(1), 151. https://doi.org/10.3390/sym15010151