nach oben

Erschienen in:

Open Access 18.04.2022

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

verfasst von: Thiago Victor Medeiros do Nascimento, Celso Augusto Guimarães Santos, Camilo Allyson Simões de Farias, Richarde Marques da Silva

Erschienen in: Water Resources Management | Ausgabe 7/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Hydrological data provide valuable information for the decision-making process in water resources management, where long and complete time series are always desired. However, it is common to deal with missing data when working on streamflow time series. Rainfall-streamflow modeling is an alternative to overcome such a difficulty. In this paper, self-organizing maps (SOM) were developed to simulate monthly inflows to a reservoir based on satellite-estimated gridded precipitation time series. Three different calibration datasets from Três Marias Reservoir, composed of inflows (targets) and 91 TRMM-estimated rainfall data (inputs), from 1998 to 2019, were used. The results showed that the inflow data homogeneity pattern influenced the rainfall-streamflow modeling. The models generally showed superior performance during the calibration phase, whereas the outcomes varied depending on the data homogeneity pattern and the chosen SOM structure in the testing phase. Regardless of the input data homogeneity, the SOM networks showed excellent results for the rainfall-runoff modeling, presenting Nash–Sutcliffe coefficients greater than 0.90.

Graphical Abstract

Vorheriger Artikel Improved Process Management of Glacial Lake Outburst Flood Hazards by Integrating Modular Monitoring, Assessment, and Simulation

Nächster Artikel Correction to: Monthly Streamflow Modeling Based on Self-organizing Maps and Satellite-estimated Rainfall Data

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abatan AA, Tett SFB, Dong B et al (2022) Drivers and physical processes of drought events over the State of São Paulo, Brazil. Clim Dyn. https://doi.org/10.1007/s00382-021-06091-2CrossRef

Adeloye AJ, Rustum R (2012) SOM and rainfall-runoff modelling in inadequately gauged basins. Hydrol Res 43(5):603–617. https://doi.org/10.2166/nh.2012.017CrossRef

Adeloye AJ, Rustum R, Kariyama D (2011) Kohonen self-organizing map estimator for the reference crop evapotranspiration. Water Resour Res 47(W08523):1–19. https://doi.org/10.1029/2011WR010690CrossRef

Alvares CA, Stape JL, Sentelhas PC, de Moraes Gonçalves JL, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorol Z 22(6):711–728. https://doi.org/10.1127/0941-2948/2013/0507CrossRef

Arnold JG et al (2012) SWAT: Model use, calibration, and validation. Trans ASABE 55(4):1491–1508

Beale M, Hagan M, Demuth H (2012) Neural network toolbox 7.0.3: User’s Guide. The MathWorks Inc, Natick, USA, p 404

Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35(4):1191–1197. https://doi.org/10.1029/1998WR900086

Ciğizoğlu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68. https://doi.org/10.1016/j.advengsoft.2005.05.002CrossRef

Ciğizoğlu HK, Kisi Ö (2005) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36(1):1–16CrossRef

Collischonn W, Dornelles F (2015) Hidrologia para engenharias e ciências ambientais. Associação Brasileira de Recursos Hídricos – ABRH. 2nd Edition. Porto Alegre, Brazil, p 350

Coulibaly P, Anctil F, Aravena R, Bobde B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896. https://doi.org/10.1029/2000WR900368CrossRef

Da Silva Filho JS, Farias C (2018) Stochastic modeling of monthly river flows by Self-Organizing Maps. J Urban Environ Eng 12(2):219–230. https://doi.org/10.4090/juee.2018.v12n2.219230CrossRef

Farias CAS, Bezerra UA, Da Silva Filho JA (2015) Runoff-erosion modeling at micro-watershed scale: a comparison of self-organizing maps structures. Geoenviron Disasters 2:14. https://doi.org/10.1186/s40677-015-0022-9CrossRef

Farias CAS, Carneiro, TC, Lourenço, AMG (2012) Mapas auto-organizáveis para modelagem chuva-vazão. Proceedings of the XI Simpósio de Recursos Hídricos do Nordeste, João Pessoa, Brazil, 1–14

Farias CAS, Santos CAG (2014) The use of Kohonen neural networks for runoff-erosion modeling. J Soils Sediments 14:1242–1250. https://doi.org/10.1007/s11368-013-0841-9CrossRef

Farias CAS, Santos CAG, Lourenço AMG, Carneiro TC (2013) Kohonen neural networks for rainfall-runoff modeling: Case study of Piancó River Basin. J Urban Environ Eng 7(1):176–182. https://doi.org/10.4090/juee.2013.v7n1.176182CrossRef

Freiwan M, Cigizoglu HK (2005) Prediction of total monthly rainfall in Jordan using feed forward backpropagation method. Fresenius Environ Bull 14(2):142–151

Gadelha AN, Coelho VHR, Xavier AC, Barbosa LR, Melo DCD, Xuan Y, Huffman GJ, Petersen WA, Almeida CD (2019) Grid box-level evaluation of IMERG over Brazil at various space and time scales. Atmos Res 218:231–244. https://doi.org/10.1016/j.atmosres.2018.12.001CrossRef

Gao Q, Li G, Bao J, Wang J (2021) Regional frequency analysis based on precipitation regionalization accounting for temporal variability and a nonstationary index flood model. Water Resour Manage 35:4435–4456. https://doi.org/10.1007/s11269-021-02959-4CrossRef

García HL, González IM (2004) Self-organizing map and clustering for wastewater treatment monitoring. Eng Appl Artif Intell 17(3):215–225. https://doi.org/10.1016/j.engappai.2004.03.004CrossRef

Gazzaz NM, Yusoff MK, Aris AZ, Juahir H, Ramli MF (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 64(11):2409–2420. https://doi.org/10.1016/j.marpolbul.2012.08.005CrossRef

Gholami V, Khaleghi MR, Pirasteh S, Booij MJ (2022) Comparison of self-organizing map, artificial neural network, and co-active neuro-fuzzy inference system methods in simulating groundwater quality: Geospatial artificial intelligence. Water Resour Manage 36:451–469. https://doi.org/10.1007/s11269-021-02969-2CrossRef

Hall T, Brooks HE, Doswell CA III (1999) Precipitation forecasting using a neural network. Weather Forecast 14(3):338–345. https://doi.org/10.1175/1520-0434(1999)014%3c0338:PFUANN%3e2.0.CO;2CrossRef

Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River, USA, p 842

Honorato AGSM, Silva GBL, Santos CAG (2018) Monthly streamflow forecasting using neuro-wavelet techniques and input analysis. Hydrol Sci J 63:15–16. https://doi.org/10.1080/02626667.2018.1552788CrossRef

Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modelling of the rainfall runoff process. Water Resour Res 31(10):2517–2530. https://doi.org/10.1029/95WR01955CrossRef

Ismail S, Shabri A, Samsudin R (2012) A hybrid model of self organizing maps and least square support vector machine for river flow forecasting. Hydrol Earth Syst Sci 16:4417–4433. https://doi.org/10.5194/hess-16-4417-2012CrossRef

Jain A, Sudheer KP, Srinivasulu S (2004) Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol Process 18(3):571–581. https://doi.org/10.1002/hyp.5502CrossRef

Kisi Ö (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63. https://doi.org/10.1061/(ASCE)1084-0699(2004)9:1(60)CrossRef

Klink CA, Machado RB (2005) Conservation of the Brazilian Cerrado. Conserv Biol 19:707–713. https://doi.org/10.1111/j.1523-1739.2005.00702.xCrossRef

Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69. https://doi.org/10.1007/BF00337288CrossRef

Kumar DN, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural network. Water Resour Manage 18(2):143–161. https://doi.org/10.1023/B:WARM.0000024727.94701.12CrossRef

Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)CrossRef

Lanna AE, Schwarzbach M (1989) MODHAC - Modelo Hidrológico Auto-Calibrável. Recursos Hídricos, Publicação 21. Pós-Graduação em Recursos Hídricos e Saneamento, Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, Brazil

Lee CM, Choi H, Kim Y, Kim M, Kim H, Hamm S (2021) Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone. Sci Total Environ 800:1–13. https://doi.org/10.1016/j.scitotenv.2021.149632CrossRef

Lettenmaier DP, Wood EF (1993) Hydrologic Forecasting. In: Maidment DR (ed) Handbook of Hydrology (pp. 26.1–26.30). New York: McGraw-Hill Inc

Li J, Shi Z, Wang G, Liu F (2020) Evaluating spatiotemporal variations of groundwater quality in northeast Beijing by self-organizing map. Water 12(5):1–15. https://doi.org/10.3390/w12051382CrossRef

Lopes JEJ, Braga Jr BPF, Conejo JGL (1981) Simulação hidrológica: Aplicações de um modelo simplificado. Proceedings of the III Simpósio Brasileiro de Recursos Hídricos, Fortaleza, Brazil, p 42–62

Loucks DP, Beek E (2017) Water resource systems planning and management. Water Resour Syst Plan Manage Ebook: Deltares and UNESCO-IHE. https://doi.org/10.1007/978-3-319-44234-7CrossRef

Mannan A, Chaudhary S, Dhanya CT, Swamy AK (2018) Regionalization of rainfall characteristics in India incorporating climatic variables and using self-organizing maps. ISH J Hydraul Eng 24(2):147–156. https://doi.org/10.1080/09715010.2017.1400409CrossRef

Melesse AM, Ahmad S, McClaina ME, Wang X, Lim YH (2011) Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manag 98(5):855–866. https://doi.org/10.1016/j.agwat.2010.12.012CrossRef

Mirabbasi R, Kisi O, Sanikhani H, Gajbhiye Meshram S (2018) Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Comput Appl 31:6843–6862. https://doi.org/10.1007/s00521-018-3519-9CrossRef

MMA (2017) Programa de revitalização da bacia hidrográfica do Rio São Francisco. Disponível em: www.mma.gov.br. Accessed in 31 December 2019

Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resour Manage 24(9):1845–1865. https://doi.org/10.1007/s11269-009-9527-xCrossRef

Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: Performance measures and evaluation criteria. Trans ASABE (am Soc Agric Biol Eng) 58(6):1763–1785. https://doi.org/10.13031/trans.58.10715CrossRef

Muhammad W, Yang H, Lei H, Muhammad A, Yang D (2018) Improving the regional applicability of satellite precipitation products by ensemble algorithm. Remote Sens 10:577. https://doi.org/10.3390/rs10040577CrossRef

Nagy HM, Watanabe KAND, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128(6):588–595. https://doi.org/10.1061/(ASCE)0733-9429(2002)128:6(588)CrossRef

Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models I: a discussion of principles. J Hydrol 10(1):282–290. https://doi.org/10.1016/0022-1694(70)90255-6CrossRef

Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manage 20(1):77–90. https://doi.org/10.1007/s11269-006-4007-zCrossRef

Nourani V, Baghanam AH, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243. https://doi.org/10.1016/j.jhydrol.2012.10.054CrossRef

Novarini B, Brentan BM, Meirelles G, Junior EL (2019a) Optimal pressure management in water distribution networks through district metered area creation based on machine learning. Brazil J Water Resour 24:e37. https://doi.org/10.1590/2318-0331.241920180165CrossRef

Novarini B, Brentan BM, Meirelles G, Junior EL (2019b) Optimal pressure management in water distribution networks through district metered area creation based on machine learning. Brazil J Water Resour 24(37):1–11. https://doi.org/10.1590/2318-0331.241920180165CrossRef

Paiva LFG, Montenegro SM, Cataldi M (2020) Prediction of monthly flows for Três Marias reservoir (São Francisco river basin) using the CFS climate forecast model. Brazil J Water Resour 25(16):1–18. https://doi.org/10.1590/2318-0331.252020190067CrossRef

Pettitt ANA (1979) Non-parametric approach to the change-point problem. Appl Stat 28(2):126–135. https://doi.org/10.2307/2346729CrossRef

Plouffe CCF, Robertson C, Chandrapala L (2015) Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: a case study of Sri Lanka. Environ Model Softw 67:57–71. https://doi.org/10.1016/j.envsoft.2015.01.011

Santos CAG, Brasil Neto RM, Silva RM, Passos JSA (2018) Integrated spatiotemporal trends using TRMM 3B42 data for the Upper São Francisco River basin. Brazil Environ Monit Assess 190:175. https://doi.org/10.1007/s10661-018-6536-3CrossRef

Santos CAG, Freire PKMM, Silva RM, Akrami SA (2019) Hybrid wavelet neural network approach for daily inflow forecasting using tropical rainfall measuring mission data. J Hydrol Eng 24:04018062. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001725CrossRef

Santos CAG, Morais BS (2013) Identification of precipitation zones within São Francisco River basin (Brazil) by global wavelet power spectra. Hydrol Sci J 58(4):789–796. https://doi.org/10.1080/02626667.2013.778412CrossRef

Silva RM, Dantas JC, Beltrão JA, Santos CAG (2018) Hydrological simulation in a tropical humid basin in the Cerrado biome using the SWAT model. Hydrol Res 49(3):908–923. https://doi.org/10.2166/nh.2018.222CrossRef

Silverman D, Dracup JA (2000) Artificial neural networks and long-range precipitation prediction in California. J Appl Meteorol 39(1):57–66. https://doi.org/10.1175/15200450(2000)039%3c0057:ANNALR%3e2.0.CO;2CrossRef

Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality – a case study. Ecol Model 220(6):888–895. https://doi.org/10.1016/j.ecolmodel.2009.01.004CrossRef

Sugawara M (1961) Automatic calibration of the Tank-Model. Hydrol Sci J 24(3):375–388. https://doi.org/10.1080/02626667909491876CrossRef

Teng H, Rossel RAV, Shi Z, Behrens T, Chappell A, Bui E (2016) Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia. Environ Model Softw 77:156–167. https://doi.org/10.1016/j.envsoft.2015.11.024CrossRef

Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting of reference evapotranspiration by artificial neural networks. J Irrig Drain Eng 129(6):454–457. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:6(454)CrossRef

Viljanen N, Honkavaara E, Näsi R, Hakala T, Niemeläinen O, Kaivosoja J (2018) A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone. Agriculture 8(70):2018. https://doi.org/10.3390/agriculture8050070CrossRef

Voutilainen A, Arvola LMJ (2017) SOM clustering of 21-year data of a small pristine boreal lake. Knowl Manag Aquat Ecosyst 418:36. https://doi.org/10.1051/kmae/2017027CrossRef

Wang Y, Sun X (2022) Simulation and evaluation of statistical downscaling of regional daily precipitation over North China based on self-organizing maps. Atmosphere 13(86):1–23. https://doi.org/10.3390/atmos13010086CrossRef

WMO (1994) Guide to Hydrological Practices: Data Acquisition and Processing, Analysis, Forecasting and Other Applications, WMO 168. Geneva: World Meteorological Organization

Wu C, Wu X, Lu C, Sun Q, He X, Yan L, Qin T (2021) Hydrogeochemical characterization and its seasonal changes of groundwater based on self-organizing maps. Water 13:1–23. https://doi.org/10.3390/w13213065CrossRef

Yotova G, Varbanov M, Tcherkezova E, Tsakovskia S (2021) Water quality assessment of a river catchment by the composite water quality index and self-organizing maps. Ecol Ind 120:1–10. https://doi.org/10.1016/j.ecolind.2020.106872CrossRef

Zanetti SS, Sousa EF, Oliveira VP, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng 133(2):83–89. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83)CrossRef

Zhang R, Chen Y, Zhang X, Ma Q, Ren L (2022) Mapping homogeneous regions for flash floods using machine learning: a case study in Jiangxi province, China. Int J Appl Earth Obs Geoinf 108:1–12. https://doi.org/10.1016/j.jag.2022.102717CrossRef

Titel: Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data
verfasst von: Thiago Victor Medeiros do Nascimento
Celso Augusto Guimarães Santos
Camilo Allyson Simões de Farias
Richarde Marques da Silva
Publikationsdatum: 18.04.2022
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 7/2022
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-022-03147-8

Springer Professional

Abstract

Graphical Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 7/2022

Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation

Long-term stagnation monitoring using machine learning: comparison of artificial neural network model and convolution neural network model

Modified Green–Ampt Model Considering Vegetation Root Effect and Redistribution Characteristics for Slope Stability Analysis

An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network

Study on Conveyance Coefficient Influenced by Momentum Exchange Under Steady and Unsteady Flows in Compound Open Channels

Performance and Participants’ Decisions Analysis in Major Water Resources Allocation Project Based on Network Governance