{"title":"基于神经网络的水流模型输入选择方法的研究","authors":"A. B. Dariane, Mohamadreza M. Behbahani","doi":"10.1080/23249676.2022.2088631","DOIUrl":null,"url":null,"abstract":"In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.","PeriodicalId":51911,"journal":{"name":"Journal of Applied Water Engineering and Research","volume":"11 1","pages":"127 - 140"},"PeriodicalIF":1.4000,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Development of an efficient input selection method for NN based streamflow model\",\"authors\":\"A. B. Dariane, Mohamadreza M. Behbahani\",\"doi\":\"10.1080/23249676.2022.2088631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.\",\"PeriodicalId\":51911,\"journal\":{\"name\":\"Journal of Applied Water Engineering and Research\",\"volume\":\"11 1\",\"pages\":\"127 - 140\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Water Engineering and Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23249676.2022.2088631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Water Engineering and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23249676.2022.2088631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
Development of an efficient input selection method for NN based streamflow model
In this paper, using a neural network-based streamflow simulation model (NNSSM), we simulate the runoff of the Ajichai River. The selection of suitable inputs is an essential step toward developing NNSSM. For this aim, we investigate a novel application of the Genetic Classification Algorithm (GCA) as an input variable selection (IVS) method in comparison with the Self-Organizing Map (SOM) and Binary Fully Informed Particle Swarm Optimization (BFIPSO). In another innovative application, we establish Social Choice (SC) for the final ranking of selected data using SOM. Next, the model was improved by adding seasonality indexes. The results indicate the superiority of GCA. The average (maximum) Nash-Sutcliffe index for GCA was found to be 0.63(0.84), while it was 0.55(0.71) and 0.58(0.77) for SOM-SC and BFIPSO, respectively. Moreover, GCA took less than 30 min for each run, while for SOM-SC and BFIPSO at least 3 and 48 h were needed under the same circumstances.
期刊介绍:
JAWER’s paradigm-changing (online only) articles provide directly applicable solutions to water engineering problems within the whole hydrosphere (rivers, lakes groundwater, estuaries, coastal and marine waters) covering areas such as: integrated water resources management and catchment hydraulics hydraulic machinery and structures hydraulics applied to water supply, treatment and drainage systems (including outfalls) water quality, security and governance in an engineering context environmental monitoring maritime hydraulics ecohydraulics flood risk modelling and management water related hazards desalination and re-use.