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In this paper, a radial basis function (RBF)‐based model with an information processor is proposed for more accurate forecasts of hourly reservoir inflow. Firstly, based on the multilayer perceptron neural (MLP) network, an information processor is developed to pre‐process the typhoon information (namely, typhoon characteristics and rainfall) and to produce forecasts of rainfall. The forecasted rainfall and the observed inflow are then used as input to the RBF‐based model, which is a nonlinear function approximator, to produce forecasts of hourly inflow. For parameter estimation of the RBF‐based model, the fully‐supervised learning algorithm is used. Actual applications of the proposed model are performed to yield 1‐ to 6‐h ahead forecasts of inflow. To assess the improvement due to the use of the typhoon information processor, models without the typhoon information processor are constructed and compared with the proposed model. The results show that the proposed model performs the best and is capable of providing improved forecasts of hourly inflow, especially for long lead‐time. In conclusion, the proposed model with a typhoon information processor can extract useful information from typhoon characteristics and rainfall, and consequently improve the forecasting performance. Copyright © 2009 John Wiley & Sons, Ltd.\u003C\u002Fjats:p>",{"EN":115},"An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons",{"VOID":117},"10.1002\u002Fhyp.7471","PUBLICATION","VERIFIED","2024-10-06T23:33:17.549+00:00","Auto 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hybrid model that blends two non‐linear data‐driven models, i.e. an artificial neural network (ANN) and a moving block bootstrap (MBB), is proposed for modelling annual streamflows of rivers that exhibit complex dependence. In the proposed model, the annual streamflows are modelled initially using a radial basis function ANN model. The residuals extracted from the neural network model are resampled using the non‐parametric resampling technique MBB to obtain innovations, which are then added back to the ANN‐modelled flows to generate synthetic replicates. The model has been applied to three annual streamflow records with variable record length, selected from different geographic regions, namely Africa, USA and former USSR. The performance of the proposed ANN‐based non‐linear hybrid model has been compared with that of the linear parametric hybrid model. The results from the case studies indicate that the proposed ANN‐based hybrid model (ANNHM) is able to reproduce the skewness present in the streamflows better compared to the linear parametric‐based hybrid model (LPHM), owing to the effective capturing of the non‐linearities. Moreover, the ANNHM, being a completely data‐driven model, reproduces the features of the marginal distribution more closely than the LPHM, but offers less smoothing and no extrapolation value. It is observed that even though the preservation of the linear dependence structure by the ANNHM is inferior to the LPHM, the effective blending of the two non‐linear models helps the ANNHM to predict the drought and the storage characteristics efficiently. Copyright © 2007 John Wiley & Sons, Ltd.\u003C\u002Fjats:p>",{"EN":347},"A nonlinear data‐driven model for synthetic generation of annual streamflows",{"VOID":318},[123],"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fhyp.6764",[352,374,395,412],{"id":353,"sortIndex":128,"researcher":24,"roles":354,"affiliations":355,"properties":367},"7b07e45e-295d-4320-a392-011a7422258f",[],[356],{"id":357,"sortIndex":25,"affiliation":358,"properties":24},"f12752e8-2220-4dad-8861-769274870178",{"id":359,"createTime":360,"updateTime":361,"relativeEntities":362,"slug":363,"properties":364,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"1c85cea5-49b0-4418-b716-7da5f7bba050","2024-04-19T18:32:29.809+00:00","2025-02-07T03:52:04.024+00:00",[],"Department-of-Civil-Engineering-Indian-Institute-of-Science-Bangalore-560012-India",{"title":365},{"EN":366},"Department of Civil Engineering, Indian Institute of Science, Bangalore, 560012, India",{"openalex":368,"orcid":370,"title":372},{"VOID":369},"A5025677977",{"VOID":371},"https:\u002F\u002Forcid.org\u002F0000-0002-9564-0048",{"EN":373},"V. 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InProceedings International Hydrology and Water Resources Symposium Institution of Engineers Perth Australia.",{},{"id":24,"text":494,"url":24,"identifiers":495},"10.1017\u002FCBO9780511802843",{"doi":494},{"id":24,"text":497,"url":24,"identifiers":498},"10.1080\u002F02626669809492102",{"doi":497},{"id":24,"text":500,"url":24,"identifiers":501},"10.1177\u002F030913330102500104",{"doi":500},{"id":24,"text":503,"url":24,"identifiers":504},"DeRoach JN, 1989, Neural networks: an artificial intelligence approach to the analysis of clinical data, Australasian Physical & Engineering Science in Medicine, 12, 100",{},{"id":24,"text":506,"url":24,"identifiers":507},"10.1214\u002Faos\u002F1176344552",{"doi":506},{"id":24,"text":509,"url":24,"identifiers":510},"10.1007\u002F978-1-4899-4541-9",{"doi":509},{"id":24,"text":512,"url":24,"identifiers":513},"10.1061\u002F(ASCE)1084-0699(1998)3:3(203)",{"doi":512},{"id":24,"text":515,"url":24,"identifiers":516},"10.1016\u002F0370-2693(93)90738-4",{"doi":515},{"id":24,"text":518,"url":24,"identifiers":519},"10.1029\u002F95WR01955",{"doi":518},{"id":24,"text":521,"url":24,"identifiers":522},"10.1029\u002F2003WR002355",{"doi":521},{"id":24,"text":524,"url":24,"identifiers":525},"10.1029\u002F2000WR900049",{"doi":524},{"id":24,"text":527,"url":24,"identifiers":528},"10.1214\u002Faos\u002F1176347265",{"doi":527},{"id":24,"text":530,"url":24,"identifiers":531},"10.1016\u002F0167-7152(93)90035-H",{"doi":530},{"id":24,"text":533,"url":24,"identifiers":534},"10.1029\u002F95RG00343",{"doi":533},{"id":24,"text":536,"url":24,"identifiers":537},"10.1029\u002F95WR02966",{"doi":536},{"id":24,"text":539,"url":24,"identifiers":540},"LePage R, 1992, Exploring the Limits of Bootstrap",{},{"id":24,"text":542,"url":24,"identifiers":543},"Loucks DP, 1981, Water Resources Systems Planning and Analysis",{},{"id":24,"text":545,"url":24,"identifiers":546},"10.1016\u002FS1364-8152(99)00007-9",{"doi":545},{"id":24,"text":548,"url":24,"identifiers":549},"10.1162\u002Fneco.1989.1.2.281",{"doi":548},{"id":24,"text":551,"url":24,"identifiers":552},"PowellMJD.1987.Radial basis function approximations to polynomials. InProceedings of the 12th Biennial Numerical Analysis Conference Dundee;223–241.",{},{"id":24,"text":554,"url":24,"identifiers":555},"PrairieJr.2002.Long‐term salinity prediction with uncertainty analysis: application for Colorado River above Glenwood Springs CO. MS thesis University of Colorado Boulder CO.",{},{"id":24,"text":557,"url":24,"identifiers":558},"10.1029\u002F1999WR900028",{"doi":557},{"id":24,"text":560,"url":24,"identifiers":561},"10.1007\u002FBF02428426",{"doi":560},{"id":24,"text":563,"url":24,"identifiers":564},"10.5194\u002Fhess-6-641-2002",{"doi":563},{"id":24,"text":566,"url":24,"identifiers":567},"Salas JD, 1980, Applied Modeling of Hydrologic Time Series",{},{"id":24,"text":569,"url":24,"identifiers":570},"10.1016\u002F0022-1694(91)90082-S",{"doi":569},{"id":24,"text":572,"url":24,"identifiers":573},"10.1016\u002FS0378-4754(99)00016-6",{"doi":572},{"id":24,"text":575,"url":24,"identifiers":576},"10.1029\u002F96WR02839",{"doi":575},{"id":24,"text":578,"url":24,"identifiers":579},"10.1016\u002FS0022-1694(00)00168-2",{"doi":578},{"id":24,"text":581,"url":24,"identifiers":582},"10.1016\u002FS0022-1694(00)00363-2",{"doi":581},{"id":24,"text":584,"url":24,"identifiers":585},"10.1029\u002FWR018i004p00909",{"doi":584},{"id":24,"text":587,"url":24,"identifiers":588},"10.1061\u002F(ASCE)1084-0699(2003)8:3(161)",{"doi":587},{"id":24,"text":590,"url":24,"identifiers":591},"SudheerKP GosainAK RamasatriKS.2002.Comparisons between back propagation and radial basis function based neural networks in rainfall‐runoff modeling. InProceedings of the International Conference on Advances in Civil Engineering vol. 1 Kharagpur India; 449–456.",{},{"id":24,"text":593,"url":24,"identifiers":594},"10.1002\u002Fhyp.5103",{"doi":593},{"id":24,"text":596,"url":24,"identifiers":597},"10.1029\u002F97WR02429",{"doi":596},{"id":24,"text":599,"url":24,"identifiers":600},"Tong H, 1990, Nonlinear Time Series Analysis: A Dynamical Systems Perspective",{},{"id":24,"text":602,"url":24,"identifiers":603},"YevjevichV.1967.An objective approach to definitions and investigations of continental hydrologic droughts. Hydrology Paper 23 Colorado State University Fort Collins CO.",{},{"id":605,"createTime":606,"updateTime":606,"relativeEntities":607,"slug":608,"properties":609,"entityType":118,"verifyStatus":119,"verifyTime":620,"verifyNote":121,"syncStatus":23,"languages":621,"translateLanguages":24,"viewCount":25,"primaryUrl":622,"fullTextUrl":24,"authors":623,"publicationType":200,"publisherRelationship":666,"citationCount":698,"citationInfo":699,"publishDate":702,"publishYear":703,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":704,"isForceReanalyzing":332},"f67d09e6-52dc-4cbf-a96a-b6498aa8394a","2024-10-06T23:33:14.899+00:00",[],"Short-term-inflow-forecasting-using-an-artificial-neural-network-model",{"mag":610,"keywords":612,"openalex":613,"abstract":615,"title":617,"doi":619},{"VOID":611},"2080109921",{},{"VOID":614},"W2080109921",{"EN":616},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objective, an artificial neural network (ANN) model for short‐term inflow forecasting is developed and several issues associated with the use of an ANN model are examined in this study. The formulated ANN model is used to forecast 1‐ to 7‐h ahead inflows into a hydropower reservoir. The root‐mean‐squared error (RMSE), the Nash–Sutcliffe coefficient (NSC), the A information criterion (AIC), B information criterion (BIC) of the 1‐ to 7‐h ahead forecasts, and the cross‐correlation coefficient between the forecast and observed inflows are estimated. Model performance is analysed and some quantitative analysis is presented. The results obtained are satisfactory. Perceived strengths of the ANN model are the capability for representing complex and non‐linear relationships as well as being able to include more information in the model easily. Although the results obtained may not be universal, they are expected to reveal some possible problems in ANN models and provide some helpful insights in the development and application of ANN models in the field of hydrology and water resources. Copyright © 2002 John Wiley & Sons, Ltd.\u003C\u002Fjats:p>",{"EN":618},"Short‐term inflow forecasting using an artificial neural network model",{"VOID":330},"2024-10-06T23:33:14.898+00:00",[123],"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fhyp.1013",[624,645],{"id":625,"sortIndex":184,"researcher":24,"roles":626,"affiliations":627,"properties":638},"56cb44f8-d412-45e8-8e1b-e519e5d10dc1",[],[628],{"id":629,"sortIndex":25,"affiliation":630,"properties":24},"9cf2612e-6ad7-44be-b0a9-416147043149",{"id":631,"createTime":632,"updateTime":632,"relativeEntities":633,"slug":634,"properties":635,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"44eab96b-78c5-403e-a9f8-ade32b638a16","2024-10-06T23:33:14.960+00:00",[],"Department-of-Land-Management-Renmin-University-of-China-Beijing-100872-P-R-China",{"title":636},{"EN":637},"Department of Land Management, Renmin University of China, Beijing, 100872, P. R. China",{"openalex":639,"orcid":641,"title":643},{"VOID":640},"A5102902014",{"VOID":642},"https:\u002F\u002Forcid.org\u002F0009-0000-0353-564X",{"EN":644},"J. Y. 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Expert system for predicting flood flow and decision‐making on dam facilities operation.Proceedings of the International Conference on Water Resources and Environment Research: Towards the 21st Century Kyoto Japan.",{},{"id":24,"text":736,"url":24,"identifiers":737},"ItoK AkibaT SakakimaH.1995. Application of neural network in prediction of inflow into reservoirs.Proceedings of the Annual Meeting of Japan Society of Hydrology and Water Resources(in Japanese);194–195.",{},{"id":24,"text":739,"url":24,"identifiers":740},"10.1061\u002F(ASCE)0733-9496(1999)125:5(263)",{"doi":739},{"id":24,"text":742,"url":24,"identifiers":743},"10.1016\u002FS0022-1694(01)00353-5",{"doi":742},{"id":24,"text":745,"url":24,"identifiers":746},"10.1029\u002F1998WR900018",{"doi":745},{"id":24,"text":748,"url":24,"identifiers":749},"10.1016\u002FS0022-1694(99)00165-1",{"doi":748},{"id":24,"text":751,"url":24,"identifiers":752},"10.1029\u002F96WR03529",{"doi":751},{"id":24,"text":754,"url":24,"identifiers":755},"10.1029\u002F1999WR900150",{"doi":754},{"id":24,"text":757,"url":24,"identifiers":758},"10.1080\u002F02626669609491511",{"doi":757},{"id":24,"text":302,"url":24,"identifiers":760},{"doi":302},{"id":24,"text":762,"url":24,"identifiers":763},"10.1080\u002F02626669509491401",{"doi":762},{"id":24,"text":765,"url":24,"identifiers":766},"10.1016\u002F0005-1098(78)90005-5",{"doi":765},{"id":24,"text":768,"url":24,"identifiers":769},"10.1016\u002FS0022-1694(98)00273-X",{"doi":768},{"id":24,"text":771,"url":24,"identifiers":772},"10.1061\u002F(ASCE)0733-9496(1995)121:6(499)",{"doi":771},{"id":24,"text":774,"url":24,"identifiers":775},"10.1002\u002F1099-1085(20001015)14:14\u003C2473::AID-HYP109>3.0.CO;2-J",{"doi":774},{"id":24,"text":777,"url":24,"identifiers":778},"10.1061\u002F(ASCE)0887-3801(2000)14:2(109)",{"doi":777},{"id":24,"text":780,"url":24,"identifiers":781},"10.1016\u002FS0022-1694(98)00242-X",{"doi":780},{"id":783,"createTime":784,"updateTime":784,"relativeEntities":785,"slug":786,"properties":787,"entityType":118,"verifyStatus":119,"verifyTime":784,"verifyNote":121,"syncStatus":23,"languages":799,"translateLanguages":24,"viewCount":25,"primaryUrl":800,"fullTextUrl":24,"authors":801,"publicationType":200,"publisherRelationship":863,"citationCount":895,"citationInfo":896,"publishDate":899,"publishYear":239,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":900,"isForceReanalyzing":332},"e9eaa9e9-5df8-4146-a441-89a436134e40","2024-10-06T23:33:14.746+00:00",[],"Auto-configuring-radial-basis-function-networks-for-chaotic-time-series-and-flood-forecasting",{"mag":788,"keywords":790,"openalex":791,"abstract":793,"title":795,"doi":797},{"VOID":789},"2026876466",{},{"VOID":792},"W2026876466",{"EN":794},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>The learning strategy of the radial basis function network (RBFN) commonly uses a hybrid learning process to identify the structure and then proceed to search the model parameters, which is a time‐consuming procedure. We proposed an evolutionary way to automatically configure the structure of RBFN and search the optimal parameters of the network. The strategy can effectively identify an appropriate structure of the network by the orthogonal least squares algorithm and then systematically search the optimal locations of centres and the widths of their corresponding kernel function by the genetic algorithm. The proposed strategy of auto‐configuring RBFN is first testified in predicting the future values of the chaotic Mackey‐Glass time series. The results demonstrate the superiority, on both effectiveness and efficiency, of the proposed strategy in predicting the chaotic time series. We then further investigate the model's suitability and reliability in flood forecast. The Lan‐Young River in north‐east Taiwan is used as a case study, where the hourly river flow of 23 flood events caused by typhoons or storms is used to train and validate the neural networks. The back propagation neural network (BPNN) is also performed for the purpose of comparison. The results demonstrate that the proposed RBFN has much better performance than the BPNN. The RBFN not only provides an efficient way to model the rainfall‐runoff process but also gives reliable and precise one‐hour and two‐hour ahead flood forecasts. Copyright © 2009 John Wiley & Sons, Ltd.\u003C\u002Fjats:p>",{"EN":796},"Auto‐configuring radial basis function networks for chaotic time series and flood 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To plan for sustainable management of the plantation forest cycle, an understanding is required of the flow pathways and hydrochemical routing signatures of the organic and mineral soils that make up the source areas for runoff. A tentative mixing model, based on simple water chemistry exists for the major (terrestrial) sources and buffers of acidification; it is being expanded and consolidated by a detailed approach to the organic components of runoff, via sampling and analysis of the luminescence of surface waters at the catchment outlet and in two distinctive feeder streams. Luminescence measurements are presented that permit a simple apportionment of source areas. However, the technique also appears to have potential for identifying differential flow sourcing between the acrotelm and catotelm of intact peat deposits and for clarifying the influence of forest root systems in altering the organic chemistry of infiltrating waters. 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Unpublished MSc thesis Deptartment of Civil Engineering University of Newcastle upon Tyne.",{},{"id":24,"text":1644,"url":24,"identifiers":1645},"10.1016\u002FS0022-1694(99)00010-4",{"doi":1644},{"id":24,"text":1647,"url":24,"identifiers":1648},"10.1016\u002FS0016-7061(97)00033-5",{"doi":1647},{"id":24,"text":1650,"url":24,"identifiers":1651},"10.1016\u002F0022-1694(90)90123-F",{"doi":1650},{"id":24,"text":1653,"url":24,"identifiers":1654},"Chapra SC, 1999, Organic carbon and surface water quality modelling, Progress in Environmental Science, 1, 49",{},{"id":24,"text":1656,"url":24,"identifiers":1657},"10.1016\u002F0304-4203(95)00062-3",{"doi":1656},{"id":24,"text":1659,"url":24,"identifiers":1660},"10.1002\u002F(SICI)1099-1085(200003)14:4\u003C701::AID-HYP967>3.0.CO;2-2",{"doi":1659},{"id":24,"text":1662,"url":24,"identifiers":1663},"10.5194\u002Fhess-1-639-1997",{"doi":1662},{"id":24,"text":1665,"url":24,"identifiers":1666},"FurzeMT WinderJM SymesJL ClarkeRT.1991.The Faunal Richness of Headwater Streams: Stage 1—Review of Existing Information. R&D 08Y National Rivers Authority: Bristol.",{},{"id":24,"text":1668,"url":24,"identifiers":1669},"10.1016\u002FS0269-7491(99)00097-4",{"doi":1668},{"id":24,"text":1671,"url":24,"identifiers":1672},"HaileSM.1990.Stream Ecology in Kielder Forest. Report to Northumbrian Water\u002FForestry Commission Department of Civil Engineering University of Newcastle upon Tyne.",{},{"id":24,"text":1674,"url":24,"identifiers":1675},"10.5194\u002Fhess-1-697-1997",{"doi":1674},{"id":24,"text":1677,"url":24,"identifiers":1678},"HindPD.1992.The Coalburn experimental catchment study: an evaluation of process hydrology at canopy closure using solute chemistry. Unpublished PhD thesis Department of Geography University of Newcastle upon Tyne.",{},{"id":24,"text":1680,"url":24,"identifiers":1681},"10.1007\u002F978-94-009-1894-8_20",{"doi":1680},{"id":24,"text":1683,"url":24,"identifiers":1684},"10.1038\u002F297300a0",{"doi":1683},{"id":24,"text":1686,"url":24,"identifiers":1687},"10.1002\u002Fhyp.3360080406",{"doi":1686},{"id":24,"text":1689,"url":24,"identifiers":1690},"Kullberg A, 1993, The ecological significance of dissolved organic carbon in acidified waters, Ambio, 22, 331",{},{"id":24,"text":1692,"url":24,"identifiers":1693},"McNishJH ShacklockJ ButcherDP LabadzJC.1996.Relative Sensitivity to Acidification of the Kielder Region Northumbria. Centre for Water and Environmental Management University of Huddersfield.",{},{"id":24,"text":1695,"url":24,"identifiers":1696},"10.1191\u002F095968398673885510",{"doi":1695},{"id":24,"text":1698,"url":24,"identifiers":1699},"10.2136\u002Fsssaj1988.03615995005200040021x",{"doi":1698},{"id":24,"text":1701,"url":24,"identifiers":1702},"10.1021\u002Fes960132l",{"doi":1701},{"id":24,"text":1704,"url":24,"identifiers":1705},"MounseySC.1999.Hydrological pathways and acid episodes in the Coalburn catchment. Unpublished PhD thesis Department of Geography University of Newcastle upon Tyne.",{},{"id":24,"text":1707,"url":24,"identifiers":1708},"MounseySC NewsonMD.1995.Acid episodes in the Coalburn catchment.Proceedings 5th British Hydrological Society National Hydrological Symposium Heriot Watt University Edinburgh 4–7 September 1995 Institution of Civil Engineers; 5.17–5.27.",{},{"id":24,"text":1710,"url":24,"identifiers":1711},"10.5194\u002Fhess-1-687-1997",{"doi":1710},{"id":24,"text":1713,"url":24,"identifiers":1714},"10.1016\u002FS0016-7061(97)00110-9",{"doi":1713},{"id":24,"text":1716,"url":24,"identifiers":1717},"10.5194\u002Fhess-2-233-1998",{"doi":1716},{"id":24,"text":1719,"url":24,"identifiers":1720},"RobinsonM MooreRE NisbetTR BlackieJR.1995.From Moorland to Forest: the Coalburn Catchment Experiment. Report 133 Institute of Hydrology: Wallingford.",{},{"id":24,"text":1722,"url":24,"identifiers":1723},"10.1002\u002Fhyp.3360060208",{"doi":1722},{"id":24,"text":1725,"url":24,"identifiers":1726},"10.1097\u002F00010694-199110000-00004",{"doi":1725},{"id":24,"text":1728,"url":24,"identifiers":1729},"Senesi N, 1993, Organic Substances in soil and Water: Natural Constituents and their Influences on Contaminant Behaviour, 74",{},{"id":24,"text":1731,"url":24,"identifiers":1732},"Smith CMS, 1993, Sensitivity to acid deposition of dystrophic peat in Great Britain, Ambio, 22, 22",{},{"id":24,"text":1734,"url":24,"identifiers":1735},"10.1016\u002F0022-1694(94)02677-4",{"doi":1734},{"id":24,"text":1737,"url":24,"identifiers":1738},"10.1002\u002F(SICI)1099-1085(200003)14:4\u003C747::AID-HYP970>3.0.CO;2-0",{"doi":1737},{"id":24,"text":1740,"url":24,"identifiers":1741},"WaterfallBJ.1994.The effect of unafforested riparian zones in a coniferous plantation on water quality. Unpublished BSc dissertation Department of Geography University of Newcastle upon Tyne.",{},{"id":24,"text":1743,"url":24,"identifiers":1744},"10.1002\u002Fhyp.3360010109",{"doi":1743},{"id":1746,"createTime":1747,"updateTime":1748,"relativeEntities":1749,"slug":1750,"properties":1751,"entityType":118,"verifyStatus":119,"verifyTime":1747,"verifyNote":121,"syncStatus":23,"languages":1764,"translateLanguages":24,"viewCount":25,"primaryUrl":1765,"fullTextUrl":24,"authors":1766,"publicationType":200,"publisherRelationship":1796,"citationCount":25,"citationInfo":1829,"publishDate":1831,"publishYear":1832,"citationAnalyzeStatus":1833,"lastCitationAnalyze":1748,"indexDatabases":24,"openAccess":24,"references":1834,"isForceReanalyzing":332},"839ac035-382a-4de0-80f4-ab8ade6a1fe3","2024-10-06T23:33:16.679+00:00","2026-06-17T22:50:06.603+00:00",[],"Time-series-forecasting-by-combining-the-radial-basis-function-network-and-the-self-organizing-map",{"mag":1752,"keywords":1754,"openalex":1755,"abstract":1757,"title":1759,"doi":1761,"gsPaper":1762},{"VOID":1753},"2145868616",{},{"VOID":1756},"W2145868616",{"EN":1758},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Based on a combination of a radial basis function network (RBFN) and a self‐organizing map (SOM), a time‐series forecasting model is proposed. Traditionally, the positioning of the radial basis centres is a crucial problem for the RBFN. In the proposed model, an SOM is used to construct the two‐dimensional feature map from which the number of clusters (i.e. the number of hidden units in the RBFN) can be figured out directly by eye, and then the radial basis centres can be determined easily. The proposed model is examined using simulated time series data. The results demonstrate that the proposed RBFN is more competent in modelling and forecasting time series than an autoregressive integrated moving average (ARIMA) model. Finally, the proposed model is applied to actual groundwater head data. It is found that the proposed model can forecast more precisely than the ARIMA model. For time series forecasting, the proposed model is recommended as an alternative to the existing method, because it has a simple structure and can produce reasonable forecasts. Copyright © 2005 John Wiley & Sons, Ltd.\u003C\u002Fjats:p>",{"EN":1760},"Time series forecasting by combining the radial basis function network and the self‐organizing 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GEP, 1976, Time Series Analysis: Forecasting and Control",{},{"id":24,"text":1845,"url":24,"identifiers":1846},"Bras RL, 1985, Random Functions and Hydrology",{},{"id":24,"text":1848,"url":24,"identifiers":1849},"10.1007\u002F978-1-4899-0004-3",{"doi":1848},{"id":24,"text":1851,"url":24,"identifiers":1852},"Broomhead DS, 1988, Multivariable functional interpolation and adaptive networks, Complex Systems, 2, 321",{},{"id":24,"text":1854,"url":24,"identifiers":1855},"10.1016\u002F0305-0483(95)00011-C",{"doi":1854},{"id":24,"text":1857,"url":24,"identifiers":1858},"Clements MP, 1993, On the limitations of comparing mean square forecast errors, Journal of Forecasting, 12, 615",{},{"id":24,"text":1860,"url":24,"identifiers":1861},"10.1007\u002FBF02551274",{"doi":1860},{"id":24,"text":512,"url":24,"identifiers":1863},{"doi":512},{"id":24,"text":1865,"url":24,"identifiers":1866},"10.1016\u002F0169-2070(92)90009-X",{"doi":1865},{"id":24,"text":1868,"url":24,"identifiers":1869},"Gardner ES, 1983, The trade‐offs in choosing a time series method, Journal of Forecasting, 2, 263",{},{"id":24,"text":1871,"url":24,"identifiers":1872},"10.1111\u002Fj.1475-4932.1993.tb02103.x",{"doi":1871},{"id":24,"text":1874,"url":24,"identifiers":1875},"Haykin S, 1994, Neural Networks: A Comprehensive 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J, 1989, Fast learning in networks of locally‐tuned processing units, Neural Computation, 4, 740",{},{"id":24,"text":988,"url":24,"identifiers":1924},{"doi":988},{"id":24,"text":1926,"url":24,"identifiers":1927},"10.1002\u002F(SICI)1097-4571(199702)48:2\u003C157::AID-ASI6>3.0.CO;2-X",{"doi":1926},{"id":24,"text":1929,"url":24,"identifiers":1930},"Powell MJD, 1987, Algorithms for Approximation, 143",{},{"id":24,"text":1932,"url":24,"identifiers":1933},"10.1142\u002FS0129065796000166",{"doi":1932},{"id":24,"text":1935,"url":24,"identifiers":1936},"10.1002\u002Fjoc.778",{"doi":1935},{"id":24,"text":1938,"url":24,"identifiers":1939},"10.1002\u002F(SICI)1096-9918(199908)27:8\u003C783::AID-SIA573>3.0.CO;2-Y",{"doi":1938},{"id":24,"text":1941,"url":24,"identifiers":1942},"10.1016\u002FS0022-1694(96)03113-7",{"doi":1941},{"id":24,"text":1944,"url":24,"identifiers":1945},"10.1016\u002F0167-8655(96)00006-2",{"doi":1944},{"id":24,"text":1947,"url":24,"identifiers":1948},"Wasserman PD, 1993, Advanced Methods in Neural Computing",{},{"id":24,"text":1950,"url":24,"identifiers":1951},"10.1016\u002FS0169-2070(97)00044-7",{"doi":1950},{"id":24,"text":1953,"url":24,"identifiers":1954},"10.1016\u002F0893-6080(96)00088-3",{"doi":1953},{"id":1956,"createTime":1957,"updateTime":1957,"relativeEntities":1958,"slug":1959,"properties":1960,"entityType":118,"verifyStatus":119,"verifyTime":1957,"verifyNote":121,"syncStatus":23,"languages":1972,"translateLanguages":24,"viewCount":25,"primaryUrl":1973,"fullTextUrl":24,"authors":1974,"publicationType":200,"publisherRelationship":2055,"citationCount":2087,"citationInfo":2088,"publishDate":2090,"publishYear":2091,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":2092,"isForceReanalyzing":332},"537738c1-438a-479b-a976-7967f0da1c8a","2024-09-03T22:14:35.201+00:00",[],"Irrigation-impact-on-annual-water-balance-of-the-oases-in-Tarim-Basin-Northwest-China",{"mag":1961,"keywords":1963,"openalex":1964,"abstract":1966,"title":1968,"doi":1970},{"VOID":1962},"2022667453",{},{"VOID":1965},"W2022667453",{"EN":1967},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>By taking the sum of annual precipitation and lateral water input (in which irrigation water withdrawal is the main component) for water availability, the Budyko hypothesis and Fu's formula derived from it was extended to the study of oases in the Tarim Basin, Northwest China. For both long‐term (multi‐year) and annual values on water balances in the 26 oases subregions, the extended Fu's formula was confirmed. Regional patterns on water balance on the 26 oases subregions were related to change in land‐use types due to increased area for irrigation. Moreover, an empirical formula for the parameter was established to reflect the influences of change in land use on water balance. The extended Budyko framework was employed to evaluate the impact of irrigation variability on annual water balance. According to the multi‐year mean timescale, variabilities in actual evapotranspiration in the oases were mainly controlled by variability in irrigation water withdrawal rather than potential evapotranspiration. The influences of variability on potential evapotranspiration became increasingly apparent together with increases in irrigation water withdrawal. Copyright © 2010 John Wiley & Sons, Ltd.\u003C\u002Fjats:p>",{"EN":1969},"Irrigation impact on annual water balance of the oases in Tarim Basin, Northwest China",{"VOID":1971},"10.1002\u002Fhyp.7830",[123],"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fhyp.7830",[1975,1995,2012,2032],{"id":1976,"sortIndex":184,"researcher":24,"roles":1977,"affiliations":1978,"properties":1990},"94d32b23-9f13-47f5-85c1-e618964e30f1",[],[1979],{"id":1980,"sortIndex":25,"affiliation":1981,"properties":24},"f18c39ef-2343-4005-9650-cf9d150457a2",{"id":1982,"createTime":1983,"updateTime":1984,"relativeEntities":1985,"slug":1986,"properties":1987,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"9aab937c-613e-4fa6-bb8b-0802b9311b29","2024-01-18T20:52:55.582+00:00","2024-09-03T22:14:35.225+00:00",[],"State-Key-Laboratory-of-Hydro-Science-and-Engineering-Department-of-Hydraulic-Engineering-Tsinghua-University-Beijing-100084-PR-China",{"title":1988},{"VI":1989},"State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, PR China",{"openalex":1991,"title":1993},{"VOID":1992},"A5051031996",{"EN":1994},"Heping Hu",{"id":1996,"sortIndex":149,"researcher":24,"roles":1997,"affiliations":1998,"properties":2005},"0a5d8b11-9f03-40bb-a3c0-a278f71d761f",[],[1999],{"id":2000,"sortIndex":25,"affiliation":2001,"properties":24},"fbedbc5c-68d3-401a-b5da-6cec884e95ae",{"id":1982,"createTime":1983,"updateTime":1984,"relativeEntities":2002,"slug":1986,"properties":2003,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":2004},{"VI":1989},{"openalex":2006,"orcid":2008,"title":2010},{"VOID":2007},"A5044116309",{"VOID":2009},"https:\u002F\u002Forcid.org\u002F0000-0002-2383-1881",{"EN":2011},"Dawen 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