River water quality prediction method and system based on machine learning
The invention discloses a river water quality prediction method and system based on machine learning, and belongs to the technical field of river intelligent management, and the method comprises the steps of data preparation, data preprocessing, river water quality prediction model construction, opt...
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| creator | ZHAO HONGQIAO ZHANG GUOXING LIU HUIFEN YANG JING LI XIAO ZHANG NAN CHEN TONGTONG ZHANG JING HAO RONGAN SUN LEI ZHANG PENG JI KAIYAO LIU CHONG WU XIANBIN WANG XIAOYI |
| description | The invention discloses a river water quality prediction method and system based on machine learning, and belongs to the technical field of river intelligent management, and the method comprises the steps of data preparation, data preprocessing, river water quality prediction model construction, optimal river water quality prediction design and river water quality prediction. According to the method, the river water quality prediction model is constructed by adopting the depth time convolution random forest model, the time dependence is more effectively captured, the modeling capability for a nonlinear relationship is enhanced, different time scale characteristics can be better extracted through the multilayer expansion convolution, the model prediction precision and robustness are improved through the random forest subnet, and the method is more suitable for being applied to the field of river water quality prediction. The reliability of river water quality prediction is enhanced; the improved tiger whale hu |
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According to the method, the river water quality prediction model is constructed by adopting the depth time convolution random forest model, the time dependence is more effectively captured, the modeling capability for a nonlinear relationship is enhanced, different time scale characteristics can be better extracted through the multilayer expansion convolution, the model prediction precision and robustness are improved through the random forest subnet, and the method is more suitable for being applied to the field of river water quality prediction. 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According to the method, the river water quality prediction model is constructed by adopting the depth time convolution random forest model, the time dependence is more effectively captured, the modeling capability for a nonlinear relationship is enhanced, different time scale characteristics can be better extracted through the multilayer expansion convolution, the model prediction precision and robustness are improved through the random forest subnet, and the method is more suitable for being applied to the field of river water quality prediction. The reliability of river water quality prediction is enhanced; the improved tiger whale hu</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TESTING |
| title | River water quality prediction method and system based on machine learning |
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