Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ...Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.展开更多
To enhance the clustering ability of self-organization network, this paper introduces a quantum inspired self-organization clustering algorithm. First, the clustering samples and the weight values in the competitive l...To enhance the clustering ability of self-organization network, this paper introduces a quantum inspired self-organization clustering algorithm. First, the clustering samples and the weight values in the competitive layer are mapped to the qubits on the Bloch sphere, and then, the winning node is obtained by computing the spherical distance between sample and weight value. Finally, the weight values of the winning nodes and its neighborhood are updated by rotating them to the sample on the Bloch sphere until the convergence. The clustering results of IRIS sample show that the proposed approach is obviously superior to the classical self-organization network and the K-mean clustering algorithm.展开更多
With the frequent information accesses from users to the Internet, it is important to organize and allocate information resources properly on different web servers. This paper considers the following problem; Due to t...With the frequent information accesses from users to the Internet, it is important to organize and allocate information resources properly on different web servers. This paper considers the following problem; Due to the capacity limitation of each single web server, it is impossible to put all information resources on one web server. Hence it is an important problem to put them on several different servers such as: (1) the amount of information resources assigned on any server is less than its capacity; (2) the access bottleneck can be avoided. In order to solve the problem in which the access frequency is variable, this paper proposes a dynamic optimal modeling. Based on the computational complexity results, the paper further focuses on the genetic algorithm for solving the dynamic problem. Finally we give the simulation results and conclusions.展开更多
A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： dispers...A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions.展开更多
A linguistic self-organizing controller using genetic algorithm is presented, whose control policy is able to generate, develop and improve. The scaling factors can be chosen automatically.Optimizing the scaling facto...A linguistic self-organizing controller using genetic algorithm is presented, whose control policy is able to generate, develop and improve. The scaling factors can be chosen automatically.Optimizing the scaling factors by genetic algorithm instead of trial or experimental method which is often used in conventional linguistic self-organizing controller eliminates the drawback of an exhausive search of the GE*GC*GU space by human operator, and also produces the better system response and a set of better control rules. A number of simulations on linear dynamic systems as well as non-linear systems such as second order process with a random disturbance, third order process with time lags and the cart-pole balancing problem etc. are described in this paper, which shows that the controller has strong adaptive properties and gives better performance than that of the conventional linguistic self-organizing controller.展开更多
The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learn...The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.展开更多
A novel notion of self-organization whose major property is that it brings about the execution of semantic intelligence as spontaneous physico-chemical processes in an unspecified ever-changing non-uniform environment...A novel notion of self-organization whose major property is that it brings about the execution of semantic intelligence as spontaneous physico-chemical processes in an unspecified ever-changing non-uniform environment is introduced. Its greatest advantage is that the covariance of causality encapsulated in any piece of semantic intelligence is provided with a great diversity of its individuality viewed as the properties of the current response and its reproducibility viewed as causality encapsulated in any of the homeostatic patterns. Alongside, the consistency of the functional metrics, which is always Euclidean, with any metrics of the space-time renders the proposed notion of self-organization ubiquitously available.展开更多
To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC...To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing.展开更多
We examined regional empirical equations for estimating the surface concentration of particulate organic carbon （POC） in the South China Sea. These algorithms are based on the direct relationships between POC and th...We examined regional empirical equations for estimating the surface concentration of particulate organic carbon （POC） in the South China Sea. These algorithms are based on the direct relationships between POC and the blue-to-green band ratios of spectral remotely sensed reflectance, Rrs（λB）/Rrs（555）. The best error statistics among the considered formulas were produced using the power function POC （rag/ m3）=262.173 [Rrs（443）/Rrs（555）]^-0.940. This formula resulted in a small mean bias of approximately -2.52%, a normalized root mean square error of 31.1%, and a determination coefficient of 0.91. This regional empirical equation is different to the results of similar studies in other oceanic regions. Our validation results suggest that our regional empirical formula performs better than the global algorithm, in the South China Sea. The feasibility of this band ratio algorithm is primarily due to the relationship between POC and the green-to- blue ratio of the particle absorption coefficient. Colored dissolved organic matter can be an important source of noise in the band ratio formula. Finally, we applied the empirical algorithm to investigate POC changes in the southwest of Luzon Strait.展开更多
Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123,...Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123, R134a, R141b, R227ea and R245fa. Under the given conditions, the parameters including evaporating and condensing pressures, working fluid and cooling water velocities were optimized by simulated annealing algorithm. The results show that the optimal evaporating pressure increases with the heat source temperature increasing. Compared with other working fluids, R123 is the best choice for the temperature range of 100--180℃ and R141 b shows better performance when the temperature is higher than 180 ℃. Economic characteristic of system decreases rapidly with the decrease of heat source temperature. ORC system is uneconomical for the heat source temperature lower than 100℃.展开更多
The problem of constructing global view of heterogeneous information sources for information sharing is becoming more and more important due to the availability of multiple information sources within the virtual organ...The problem of constructing global view of heterogeneous information sources for information sharing is becoming more and more important due to the availability of multiple information sources within the virtual organization. Global view is defined to provide a unified representation of the information in the different sources by analyzing concept schemas associated with them and resolving possible semantic heterogeneity. An ontology-based method for global view construction is proposed. In the method, （ 1 ） Based on the formal ontologies, the concept of semantic affinity is introduced to assess the level of semantic relationship between information classes from different information sources; （2） Information classes are classified by semantic affinity levels using clustering procedures so that their different representations can be analyzed for unification; （3） Global view is constructed starting from selected clusters by unifying representation of their elements. The application example of using the method in the joint-aerial defense organization is illustrated and the result shows that the proposed method is feasible.展开更多
A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and vari...A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo forest.The auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation index.The prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling sites.Both the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK.The three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and OK.Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,respectively.In addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables.Therefore,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.展开更多
This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations ...This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations for minimization of the makespan(MSN). When tools are expensive, just a single copy of every tool kind is made available for use in the FMS system. Because the tools are housed in a central tool magazine(CTM), which then distributes and delivers them to many machines, because there is no longer a need to duplicate the tools in each machine, the associated costs are avoided. Choosing alternative machines for job operations(jb-ons), assigning tools to jb-ons, sequencing jb-ons on machines, and arranging allied trip activities, together with the TT’s loaded trip times and deadheading periods, are all challenges that must be overcome to achieve the goal of minimizing MSN. In addition to a mixed nonlinear integer programming(MNLIP) formulation for this simultaneous scheduling problem, this paper suggests a symbiotic organisms search algorithm(SOSA) for the problem’s solution. This algorithm relies on organisms’ symbiotic interaction strategies to keep living in an ecosystem. The findings demonstrate that SOSA is superior to the Jaya algorithm in providing solutions and that using alternative machines for operations helps bring down MSN.展开更多
Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the...Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information.Therefore,this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017.Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test.The successive projection algorithm(SPA),competitive adaptive reweighted sampling(CARS),and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths.Finally,partial least squares regression(PLSR)and random forest(RF)models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content.The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features(i.e.,1400.0,1900.0,and 2200.0 nm),and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0–510.0 nm.Both models can achieve a more satisfactory prediction of the SOM content,and the RF model had better accuracy than the PLSR model.The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination(R2)of 0.78 and the residual prediction deviation(RPD)of 2.38.The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content.Therefore,combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.展开更多
基金supported by the Natural Science Foundation of Shaanxi Province(2023-JC-QN-0728)the China Postdoctoral Science Foundation(2021M693942)。
文摘Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.
文摘To enhance the clustering ability of self-organization network, this paper introduces a quantum inspired self-organization clustering algorithm. First, the clustering samples and the weight values in the competitive layer are mapped to the qubits on the Bloch sphere, and then, the winning node is obtained by computing the spherical distance between sample and weight value. Finally, the weight values of the winning nodes and its neighborhood are updated by rotating them to the sample on the Bloch sphere until the convergence. The clustering results of IRIS sample show that the proposed approach is obviously superior to the classical self-organization network and the K-mean clustering algorithm.
基金Supportd by the Hi-tech Research and Development Program of China(2002AA1Z1490)
文摘With the frequent information accesses from users to the Internet, it is important to organize and allocate information resources properly on different web servers. This paper considers the following problem; Due to the capacity limitation of each single web server, it is impossible to put all information resources on one web server. Hence it is an important problem to put them on several different servers such as: (1) the amount of information resources assigned on any server is less than its capacity; (2) the access bottleneck can be avoided. In order to solve the problem in which the access frequency is variable, this paper proposes a dynamic optimal modeling. Based on the computational complexity results, the paper further focuses on the genetic algorithm for solving the dynamic problem. Finally we give the simulation results and conclusions.
基金the National Natural Science Foundation of China (70572045).
文摘A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions.
文摘A linguistic self-organizing controller using genetic algorithm is presented, whose control policy is able to generate, develop and improve. The scaling factors can be chosen automatically.Optimizing the scaling factors by genetic algorithm instead of trial or experimental method which is often used in conventional linguistic self-organizing controller eliminates the drawback of an exhausive search of the GE*GC*GU space by human operator, and also produces the better system response and a set of better control rules. A number of simulations on linear dynamic systems as well as non-linear systems such as second order process with a random disturbance, third order process with time lags and the cart-pole balancing problem etc. are described in this paper, which shows that the controller has strong adaptive properties and gives better performance than that of the conventional linguistic self-organizing controller.
文摘The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.
文摘A novel notion of self-organization whose major property is that it brings about the execution of semantic intelligence as spontaneous physico-chemical processes in an unspecified ever-changing non-uniform environment is introduced. Its greatest advantage is that the covariance of causality encapsulated in any piece of semantic intelligence is provided with a great diversity of its individuality viewed as the properties of the current response and its reproducibility viewed as causality encapsulated in any of the homeostatic patterns. Alongside, the consistency of the functional metrics, which is always Euclidean, with any metrics of the space-time renders the proposed notion of self-organization ubiquitously available.
文摘To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing.
基金Supported by the National Natural Science Foundation of China(Nos.41376042,41176035)the Natural Science for Youth Foundation(No.41206029)+2 种基金the Youth Foundation by South China Sea Institute of Oceanology,Chinese Academy of Sciences(No.SQ201102)the Open Research Fund of State Key Laboratory of Estuarine and Coastal Research(No.SKLEC-KF201302)the Open Project Program of State Key Laboratory of Tropical Oceanography,South China Sea Institute of Oceanology,Chinese Academy of Sciences(No.LTOZZ1201)
文摘We examined regional empirical equations for estimating the surface concentration of particulate organic carbon （POC） in the South China Sea. These algorithms are based on the direct relationships between POC and the blue-to-green band ratios of spectral remotely sensed reflectance, Rrs（λB）/Rrs（555）. The best error statistics among the considered formulas were produced using the power function POC （rag/ m3）=262.173 [Rrs（443）/Rrs（555）]^-0.940. This formula resulted in a small mean bias of approximately -2.52%, a normalized root mean square error of 31.1%, and a determination coefficient of 0.91. This regional empirical equation is different to the results of similar studies in other oceanic regions. Our validation results suggest that our regional empirical formula performs better than the global algorithm, in the South China Sea. The feasibility of this band ratio algorithm is primarily due to the relationship between POC and the green-to- blue ratio of the particle absorption coefficient. Colored dissolved organic matter can be an important source of noise in the band ratio formula. Finally, we applied the empirical algorithm to investigate POC changes in the southwest of Luzon Strait.
基金Project(2009GK2009) supported by Science and Technology Department Funds of Hunan Province,ChinaProject(08C26224302178) supported by Innovation Fund for Technology Based Firms of China
文摘Taking the ratio of heat transfer area to net power and heat recovery efficiency into account, a multi-objective mathematical model was developed for organic Rankine cycle （ORC）. Working fluids considered were R123, R134a, R141b, R227ea and R245fa. Under the given conditions, the parameters including evaporating and condensing pressures, working fluid and cooling water velocities were optimized by simulated annealing algorithm. The results show that the optimal evaporating pressure increases with the heat source temperature increasing. Compared with other working fluids, R123 is the best choice for the temperature range of 100--180℃ and R141 b shows better performance when the temperature is higher than 180 ℃. Economic characteristic of system decreases rapidly with the decrease of heat source temperature. ORC system is uneconomical for the heat source temperature lower than 100℃.
基金This project was supported by the National Natural Science Foundation of China (60172012) Hunan Provincial NaturalScience Foundation of China (03JJY3110) .
文摘The problem of constructing global view of heterogeneous information sources for information sharing is becoming more and more important due to the availability of multiple information sources within the virtual organization. Global view is defined to provide a unified representation of the information in the different sources by analyzing concept schemas associated with them and resolving possible semantic heterogeneity. An ontology-based method for global view construction is proposed. In the method, （ 1 ） Based on the formal ontologies, the concept of semantic affinity is introduced to assess the level of semantic relationship between information classes from different information sources; （2） Information classes are classified by semantic affinity levels using clustering procedures so that their different representations can be analyzed for unification; （3） Global view is constructed starting from selected clusters by unifying representation of their elements. The application example of using the method in the joint-aerial defense organization is illustrated and the result shows that the proposed method is feasible.
基金The article is supported by National Key Research and Development Projects of P.R.China(No.2018YFD0600100).
文摘A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo forest.The auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation index.The prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling sites.Both the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK.The three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and OK.Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,respectively.In addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables.Therefore,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.
文摘This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations for minimization of the makespan(MSN). When tools are expensive, just a single copy of every tool kind is made available for use in the FMS system. Because the tools are housed in a central tool magazine(CTM), which then distributes and delivers them to many machines, because there is no longer a need to duplicate the tools in each machine, the associated costs are avoided. Choosing alternative machines for job operations(jb-ons), assigning tools to jb-ons, sequencing jb-ons on machines, and arranging allied trip activities, together with the TT’s loaded trip times and deadheading periods, are all challenges that must be overcome to achieve the goal of minimizing MSN. In addition to a mixed nonlinear integer programming(MNLIP) formulation for this simultaneous scheduling problem, this paper suggests a symbiotic organisms search algorithm(SOSA) for the problem’s solution. This algorithm relies on organisms’ symbiotic interaction strategies to keep living in an ecosystem. The findings demonstrate that SOSA is superior to the Jaya algorithm in providing solutions and that using alternative machines for operations helps bring down MSN.
基金supported by the Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region,China(2021D01D06)the National Natural Science Foundation of China(41961059)。
文摘Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information.Therefore,this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017.Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test.The successive projection algorithm(SPA),competitive adaptive reweighted sampling(CARS),and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths.Finally,partial least squares regression(PLSR)and random forest(RF)models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content.The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features(i.e.,1400.0,1900.0,and 2200.0 nm),and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0–510.0 nm.Both models can achieve a more satisfactory prediction of the SOM content,and the RF model had better accuracy than the PLSR model.The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination(R2)of 0.78 and the residual prediction deviation(RPD)of 2.38.The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content.Therefore,combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.
