Groundwater quality assessment and prediction(GQAP)is vital for protecting groundwater resources.Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage...Groundwater quality assessment and prediction(GQAP)is vital for protecting groundwater resources.Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding.Recently,the application of machine learning(ML)in GAQP(GQAPxML)has been widely studied due to ML’s reliability and efficiency.While many GQAPxML publications exist,a thorough review is missing.This review provides a comprehensive summary of the development of ML applications in the field of GQAP.First,the workflow of ML modeling is briefly introduced,as are data preparation,model development,model evaluation,and model application.Second,299 publications related to the topic are filtered,mainly through ML modeling.Subsequently,many aspects of GQAPxML,such as publication trends,the spatial distribution of study areas,the size of data sets,and ML algorithms,are discussed from a bibliometric perspective.In addition,we review in detail the well-established applications and recent findings for several subtopics,including groundwater quality assessment,groundwater quality modeling using groundwater quality parameters,groundwater quality spatial mapping,probability estimation of exceeding the groundwater quality threshold,groundwater quality temporal prediction,and the hybrid use of ML and physics-based models.Finally,the development of GQAPxML is explored from three perspectives:data collection and preprocessing,model building and evaluation,and the broadening of model applications.This review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.展开更多
为全面客观分析土壤质量评价领域的研究动态和发展趋势,利用知识图谱工具HistCite Pro 2.1、VOSviewer 1.6.19和CiteSpace 6.1.R6软件,基于Web of Science核心合集数据库,就近10年(2012—2022年)土壤质量评价领域的发文量、高被引文章...为全面客观分析土壤质量评价领域的研究动态和发展趋势,利用知识图谱工具HistCite Pro 2.1、VOSviewer 1.6.19和CiteSpace 6.1.R6软件,基于Web of Science核心合集数据库,就近10年(2012—2022年)土壤质量评价领域的发文量、高被引文章、研究热点和研究趋势等进行计量分析。结果显示,近10年土壤质量评价领域的发文量呈上升趋势。关键词聚类网络划分出土壤健康评价、土壤质量评价、微生物指标3类。应用机器学习算法评价土壤质量、筛选微生物指标构建评价最小数据集是当前土壤质量评价领域的两大研究热点。将机器学习模型应用于不同土壤类型、种植系统和管理措施下评价土壤质量,挖掘土壤核心功能微生物和优势菌种作为微生物评价指标是未来的研究趋势。展开更多
The behavior of schools of zebrafish(Danio rerio) was studied in acute toxicity environments.Behavioral features were extracted and a method for water quality assessment using support vector machine(SVM) was de-velope...The behavior of schools of zebrafish(Danio rerio) was studied in acute toxicity environments.Behavioral features were extracted and a method for water quality assessment using support vector machine(SVM) was de-veloped.The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration(LC50) of a pollutant.The data were used to develop a method to evaluate water quality,so as to give an early indication of toxicity.Four kinds of metal ions(Cu2+,Hg2+,Cr6+,and Cd2+) were used for toxicity testing.To enhance the efficiency and accuracy of assessment,a method combining SVM and a genetic algorithm(GA) was used.The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable.The method gave satisfactory results for a variety of metal pollutants,demonstrating that this is an effec-tive approach to the classification of water quality.展开更多
We propose a systematic ECG quality classification method based on a kernel support vector machine(KSVM) and genetic algorithm(GA) to determine whether ECGs collected via mobile phone are acceptable or not. This metho...We propose a systematic ECG quality classification method based on a kernel support vector machine(KSVM) and genetic algorithm(GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function(GRBF) is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function(MHWF). GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search(GS) method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive(TP), false positive(FP), and classification accuracy were used as the assessment indices. For training database set A(1000 recordings), the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were: TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B(500 recordings), the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.展开更多
An image and video quality assessment method was developed using neural network and support vector machines （SVM） with the peak signal to noise ratio （PSNR） and the structure similarity indexes used to describe im...An image and video quality assessment method was developed using neural network and support vector machines （SVM） with the peak signal to noise ratio （PSNR） and the structure similarity indexes used to describe image quality. The neural network was used to obtain the mapping functions between the objective quality assessment indexes and subjective quality assessment. The SVM was used to classify the images into different types which were accessed using different mapping functions. Video quality was assessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. Simulation results show that the method accurately accesses image quality. The monotonicity of the method for images is 6.94% higher than with the PSNR method, and the root mean square error is at least 35.90% higher than with the PSNR.展开更多
基金supported by the Ministry of Science and Technology of the People’s Republic of China(Nos.2019YFC1803900 and 2018ZX07109-002).
文摘Groundwater quality assessment and prediction(GQAP)is vital for protecting groundwater resources.Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding.Recently,the application of machine learning(ML)in GAQP(GQAPxML)has been widely studied due to ML’s reliability and efficiency.While many GQAPxML publications exist,a thorough review is missing.This review provides a comprehensive summary of the development of ML applications in the field of GQAP.First,the workflow of ML modeling is briefly introduced,as are data preparation,model development,model evaluation,and model application.Second,299 publications related to the topic are filtered,mainly through ML modeling.Subsequently,many aspects of GQAPxML,such as publication trends,the spatial distribution of study areas,the size of data sets,and ML algorithms,are discussed from a bibliometric perspective.In addition,we review in detail the well-established applications and recent findings for several subtopics,including groundwater quality assessment,groundwater quality modeling using groundwater quality parameters,groundwater quality spatial mapping,probability estimation of exceeding the groundwater quality threshold,groundwater quality temporal prediction,and the hybrid use of ML and physics-based models.Finally,the development of GQAPxML is explored from three perspectives:data collection and preprocessing,model building and evaluation,and the broadening of model applications.This review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.
文摘为全面客观分析土壤质量评价领域的研究动态和发展趋势,利用知识图谱工具HistCite Pro 2.1、VOSviewer 1.6.19和CiteSpace 6.1.R6软件,基于Web of Science核心合集数据库,就近10年(2012—2022年)土壤质量评价领域的发文量、高被引文章、研究热点和研究趋势等进行计量分析。结果显示,近10年土壤质量评价领域的发文量呈上升趋势。关键词聚类网络划分出土壤健康评价、土壤质量评价、微生物指标3类。应用机器学习算法评价土壤质量、筛选微生物指标构建评价最小数据集是当前土壤质量评价领域的两大研究热点。将机器学习模型应用于不同土壤类型、种植系统和管理措施下评价土壤质量,挖掘土壤核心功能微生物和优势菌种作为微生物评价指标是未来的研究趋势。
基金Project supported by the Natural Science Foundation of Ningbo City (No.2010A610005)the Key Science and Technology Program of Zhejiang Province (No.2011C11049),China
文摘The behavior of schools of zebrafish(Danio rerio) was studied in acute toxicity environments.Behavioral features were extracted and a method for water quality assessment using support vector machine(SVM) was de-veloped.The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration(LC50) of a pollutant.The data were used to develop a method to evaluate water quality,so as to give an early indication of toxicity.Four kinds of metal ions(Cu2+,Hg2+,Cr6+,and Cd2+) were used for toxicity testing.To enhance the efficiency and accuracy of assessment,a method combining SVM and a genetic algorithm(GA) was used.The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable.The method gave satisfactory results for a variety of metal pollutants,demonstrating that this is an effec-tive approach to the classification of water quality.
基金Project supported by the National Natural Science Foundation of China(Nos.51075243 and 61201049)the Excellent Young Scientist Awarded Foundation of Shandong Province,China(No.BS2013DX029)the China Postdoctoral Science Foundation(No.2013M530323)
文摘We propose a systematic ECG quality classification method based on a kernel support vector machine(KSVM) and genetic algorithm(GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function(GRBF) is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function(MHWF). GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search(GS) method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive(TP), false positive(FP), and classification accuracy were used as the assessment indices. For training database set A(1000 recordings), the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were: TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B(500 recordings), the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.
文摘An image and video quality assessment method was developed using neural network and support vector machines （SVM） with the peak signal to noise ratio （PSNR） and the structure similarity indexes used to describe image quality. The neural network was used to obtain the mapping functions between the objective quality assessment indexes and subjective quality assessment. The SVM was used to classify the images into different types which were accessed using different mapping functions. Video quality was assessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. Simulation results show that the method accurately accesses image quality. The monotonicity of the method for images is 6.94% higher than with the PSNR method, and the root mean square error is at least 35.90% higher than with the PSNR.
