Analysis of Soil Salinity Based on Spectrum and RVIPSO-MELM
SONG Ni-na1, XIAO Dong1*, LI Sen1, GAO Yu-jie2
1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2. School of Metallurgy, Northeastern University, Shenyang 110819, China
Abstract：Studying, the nature and composition of saline soil, are significant to the ecological environment. Most traditional methods for determining salt content are based on chemical analysis. Due to their high cost and low efficiency, the feasibility of applying them to large areas of land is greatly reduced. An extreme learning machine (ELM), as a machine learning system based on a feedforward neural network, has been successfully used as a spectral processing method in many studies. In order to improve the traditional salt content detection methods of saline-alkali soils, this paper uses spectroscopy combined with an improved extreme learning machine (ELM) model to study saline-alkali soils, further expanding the application scenarios of spectroscopy analysis methods. We obtain the corresponding spectral reflectance and salt content data according to the 62 surface samples collected in Zhenlai County and then propose the multi-layer extreme learning machine model optimized by improved particle swarm optimization (PSO) algorithm with improved particle swarm optimization (PSO) algorithm with random values(RVIPSO-MELM) model. Firstly, we use the principal component analysis(PCA) to extract the characteristics of the spectral data and then adopt the ELM algorithm to establish a classification model for the spectral data. Finally, to improve the accuracy and speed, an improved particle swarm optimization algorithm is applied. This model combines the advantages of both multi-layer ELM with random values (RV-MELM) and the multi-layer ELM model optimized by an improved PSO algorithm (IPSO-MELM), using the heuristic algorithm to search for the optimal value and also having randomness, which improves the speed of model optimization. The parameters are optimized and selected to improve the performance of the model. Moreover, the model can be extended to multiple layers, and the two methods of selecting parameters between hidden layers, calculated by empirical formulas or improved heuristic algorithm, are discussed about the model’s performance and optimize the time. The practical results show that it is a more realistic method to select the parameters of the first layer to use the improved particle swarm optimization algorithm and determine the parameters of the subsequent hidden layers by using the empirical formula calculate. Before the heuristic search for the optimal value, the Monte Carlo method is applied to determine a better initial value, enabling the model to maintain a high accuracy rate and further improving the optimization speed. Compared with traditional methods, this spectral analysis combined with the ELM model saves time and economic costs, giving it a certain promotion significance.
宋妮娜，肖 冬，李 森，高宇洁. 基于光谱和改进极限学习机的土壤含盐量分析[J]. 光谱学与光谱分析, 2022, 42(08): 2482-2487.
SONG Ni-na, XIAO Dong, LI Sen, GAO Yu-jie. Analysis of Soil Salinity Based on Spectrum and RVIPSO-MELM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2482-2487.
[1] ZHU Wan-xue, SUN Zhi-gang, LI Bin-bin, et al（朱婉雪, 孙志刚, 李彬彬，等）. Journal of Geoinformation Science（地球信息科学学报），2021, 23(3): 536.
[2] Raghuwanshi B S, Shukla Singh. Knowledge-Based System, 2020, 187: 104814.
[3] Dai H, Cao J, Wang T, et al. Neural Networks，2019，115：11.
[4] Huang G, Huang G B, Song S, et al. Neural Networks，2015，61：32.
[5] Liang J, Yan C, Zhang Y, et al. Chemometrics Intelligent Laboratory Syst.，2020，197：103930.
[6] Chen H, Tan C, Lin Z. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy，2020，229：117982.
[7] Xiao Dong, Wan Lushan, Sun Xiaoyu. Optics & Laser Technology，2019，114：10.
[8] Xiao Dong, Liu Chongmin, LE Ba Tuan. Infrared Phys. Technol.，2019，97：341.
[9] Huang G B, Chen L, Siew C K. IEEE Trans Neural Network，2016，17(4)：879.
[10] Ba Tuan Le, Dong Xiao, et al. IEEE Transaction on Geoscience and Remote Sensing, 2019，57：7.
[11] Feng Guorui, Huang Guangbin, Lin Qingping, et al. IEEE Transactions on Neural Networks, 2009, 20(8): 1352.
