## 4 Results
### Results of Predicting the Cumulative Ground Deformation Using BP  
As indicated in the methodology, the data were normalized to the scale from 0 to 1 to improve the prediction precision. Then, the independent variables of temperature, precipitation, water level, and building area were used to calculate relationships with the dependent variable cumulative ground deformation using BP. The independent variables were set as input parameters, and the cumulative ground deformation was set as the output parameter. The number of hidden nodes was calculated using Equation (5), and the optimal set of the number of hidden nodes was confirmed through a variety of experiments. The final set of numbers of hidden notes was eight. The study rate will influence both the study speed and study precision; when the study rate is small, the time will be longer, and the precision will be higher; while in contrast, when the study rate is large, the time will be shorter, and the precision will be less. Besides, as the times of training increased, the errors of samples were actually not decreasing, or even growing bigger. Therefore, there is no necessity to training anymore with this condition, because overtraining will probably lead to overfitting. In order to solve this problem, the training will continue constantly until one of the following conditions was satisfied: (1) the iteration is 100 times; (2) the mean square error of the target of the sample training is below 0.001; (3) in the process of the network using training samples, the error curve of the validated sample will not decline  
Figure 10: R\({}^{2}\) between Influencing Factors and Cumulative Sedimentation. (**a**) correlations between the cumulative temperature and cumulative deformation; (**b**) correlations between the cumulative precipitation and cumulative deformation; (**c**) correlations between the building area and cumulative deformation; and (**d**) correlations between the water level and cumulative deformation. for six consecutive iterations. In this study, the error curve of the validated sample did not decrease after more than six iterations. Therefore, the training network was stopped, and the time of iteration was 67. According to Figure 11, a 4-8-1 BP model was built: the model was confirmed with four input parameters, eight hidden nodes, and one output parameter. The independent variables of temperature, precipitation, water level, and building area were set as input parameters, and the cumulative ground deformation was set as the output parameter. In this study, 42 sets of sample data were used to construct the model, of which 30 sets of randomly extracted data were training samples, another six sets of data were randomly extracted as validation samples, and the last six sets of data were used as test samples. Figure 12 clearly showed the fitting results were good with 42 sets of total samples, 30 sets groups of training samples, six sets of validating samples, and six other testing samples. The training sample, the comparison between the verification sample, and the test sample indicated that the constructed BP neural network model had obtained a good predictive effect. Figure 11: The Set of BP Nodes, Input Parameters, and an Output Parameter. Figure 12: Comparison of Predicted Data and Measured Data. Moreover, there was also a certain relationship between the water level and ground deformation, but the evaluation results were lower than those of the former. In the Erhai region, meteorological elements and water-level data were consistent across the research area; however, the ground deformation results of the southern and northern regions were completely different, which indicated the important influence of the building expansion during the process of ground deformation. The northern part of Erhai was mainly cultivated by crops, and the urban distribution was relatively scattered. There were no large areas of urban agglomeration.