Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow
Different statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbour non-parametric regression method (K-NN) due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes...
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| Format: | Article |
| Language: | English |
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Water and Wastewater Consulting Engineers Research Development
2012-07-01
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| Series: | آب و فاضلاب |
| Subjects: | |
| Online Access: | http://www.wwjournal.ir/article_1666_20f0363abdb36eef20226078e5da8a3f.pdf |
| _version_ | 1818852920718786560 |
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| author | Mohammad Azmi Shahab Araghinejad |
| author_facet | Mohammad Azmi Shahab Araghinejad |
| author_sort | Mohammad Azmi |
| collection | DOAJ |
| description | Different statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbour non-parametric regression method (K-NN) due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes. In this study, K-NN method is explained completely. Besides, development and improvement approaches such as best neighbour estimation, data transformation functions, distance functions and proposed extrapolation method are described. K-NN method in company with its development approaches is used in streamflow forecasting of Zayandeh-Rud Dam upper basin. Comparing between final results of classic K-NN method and modified K-NN (number of neighbour 5, transformation function of Range Scaling, distance function of Mahanalobis and proposed extrapolation method) shows that modified K-NN in criteria of goodness of fit, root mean square error, percentage of volume of error and correlation has had performance improvement 45% , 59% and 17% respectively. These results approve necessity of applying mentioned approaches to derive more accurate forecasts. |
| first_indexed | 2024-12-19T07:28:35Z |
| format | Article |
| id | doaj.art-18ee2db72c9a47b280ef338451e2fd69 |
| institution | Directory Open Access Journal |
| issn | 1024-5936 2383-0905 |
| language | English |
| last_indexed | 2024-12-19T07:28:35Z |
| publishDate | 2012-07-01 |
| publisher | Water and Wastewater Consulting Engineers Research Development |
| record_format | Article |
| series | آب و فاضلاب |
| spelling | doaj.art-18ee2db72c9a47b280ef338451e2fd692022-12-21T20:30:46ZengWater and Wastewater Consulting Engineers Research Developmentآب و فاضلاب1024-59362383-09052012-07-012321081191666Development of K-Nearest Neighbour Regression Method in Forecasting River Stream FlowMohammad Azmi0Shahab Araghinejad1Ph. D. Student of Water Resources Eng., College of Tech. and Agricultural Eng., Tehran University, TehranAssist. Prof. of Water Resources Eng., College of Tech. and Agricultural Eng., Tehran University, TehranDifferent statistical, non-statistical and black-box methods have been used in forecasting processes. Among statistical methods, K-nearest neighbour non-parametric regression method (K-NN) due to its natural simplicity and mathematical base is one of the recommended methods for forecasting processes. In this study, K-NN method is explained completely. Besides, development and improvement approaches such as best neighbour estimation, data transformation functions, distance functions and proposed extrapolation method are described. K-NN method in company with its development approaches is used in streamflow forecasting of Zayandeh-Rud Dam upper basin. Comparing between final results of classic K-NN method and modified K-NN (number of neighbour 5, transformation function of Range Scaling, distance function of Mahanalobis and proposed extrapolation method) shows that modified K-NN in criteria of goodness of fit, root mean square error, percentage of volume of error and correlation has had performance improvement 45% , 59% and 17% respectively. These results approve necessity of applying mentioned approaches to derive more accurate forecasts.http://www.wwjournal.ir/article_1666_20f0363abdb36eef20226078e5da8a3f.pdfNearest Neighbour MethodDistance FunctionsMahanalobis DistanceExtrapolationZayandeh-Rud River |
| spellingShingle | Mohammad Azmi Shahab Araghinejad Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow آب و فاضلاب Nearest Neighbour Method Distance Functions Mahanalobis Distance Extrapolation Zayandeh-Rud River |
| title | Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow |
| title_full | Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow |
| title_fullStr | Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow |
| title_full_unstemmed | Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow |
| title_short | Development of K-Nearest Neighbour Regression Method in Forecasting River Stream Flow |
| title_sort | development of k nearest neighbour regression method in forecasting river stream flow |
| topic | Nearest Neighbour Method Distance Functions Mahanalobis Distance Extrapolation Zayandeh-Rud River |
| url | http://www.wwjournal.ir/article_1666_20f0363abdb36eef20226078e5da8a3f.pdf |
| work_keys_str_mv | AT mohammadazmi developmentofknearestneighbourregressionmethodinforecastingriverstreamflow AT shahabaraghinejad developmentofknearestneighbourregressionmethodinforecastingriverstreamflow |