Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China

Figure 6

Scatter verification of TSM concentration values estimated by six models and measured values. (a) The BP neural network parameter is a 3-layer neural network. The network structure is 32-6-1, the number of hidden neurons is 6, the number of iterations is 1000, and the learning rate is 0.003. (b) KNN parameters include the search algorithm as brute-force algorithm for violent search, k = 4. (c) Decision tree parameters: Gini coefficient is used for feature selection, and the postpruning method is used for the tree correction. (d) AdaBoost parameters: the error calculation function uses the exponential function, and the feature division point method in the weak classifier selects best. (e) RF algorithm parameters: the number of decision trees k is 100 and the number of features m is 6. (f) GA_RF algorithm parameters: the number of decision trees k is 260 and the number of features m is 22.

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