Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method

Piasecki, A.; Jurasz, J.; Adamowski, J. F.

doi:10.1007/s11600-018-0183-5

Artykuł - szczegóły

Tytuł artykułu

Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method

Autorzy

Piasecki A. , Jurasz J. , Adamowski J. F.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-018-0183-5

Warianty tytułu

Języki publikacji

Abstrakty

Lake waters are a significant source of drinking water and contribute to the local economy (e.g. enabling irrigation, offering opportunities for tourism, waterways for transport, and meeting utility water demands); therefore, the ability to accurately forecast lake water levels is important. However, given the significant lack of research with respect to forecasting water levels in small lakes (i.e. 0.05 km2\area\10 km2), the present study sought to address this knowledge gap by testing a pair of hypotheses: (1) it is possible to forecast water levels in small surface lakes using artificial neural networks (ANN), and (2) better water-level forecasts will be obtained when the wavelet transform (WT) is used as an input data preprocessing tool. Based on an analysis of a case study in Lake Biskupinskie (1.16 km2) in Poland and based on a range of model performance statistics (e.g. mean absolute error, root mean square error, mean squared error, coefficient of determination, mean absolute percentage error), both hypotheses were confirmed for monthly forecasting of lake water levels. ANNs provided good forecasting results, and WT pre-processing of input data led to even better forecasts. Additionally, it was found that meteorological variables did not have a significant impact in forecasting water-level fluctuations. In light of the results and the limited scope of the present study, proposed future research directions and problems to be resolved are discussed.

Słowa kluczowe

glacial lake climate change wavelet transform artificial neural networks water level

jezioro polodowcowe zmiana klimatu transformacja falkowa sztuczne sieci neuronowe Poziom wód

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2018

Tom

Vol. 66, no. 5

Strony

1093--1107

Opis fizyczny

Bibiogr. 65 poz.

Twórcy

autor

Piasecki A.

adm.piasecki@gmail.com

Department of Geomatics and Cartography, Faculty of Earth Sciences, Nicolaus Copernicus University, Lwowska 1, 87-100 Toruń, Poland

autor

Jurasz J.

jakubkamiljurasz@gmail.com

Department of Engineering Management, Faculty of Management, AGH University, Gramatyka 10, 30-001 Kraków, Poland

autor

Adamowski J. F.

jan.adamowski@mcgill.ca

Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada

Bibliografia

1. Abrahart RJ, Mount NJ, Shamseldin AY (2012) Neuroemulation: definition and key benefits for water resources research. Hydrol Sci J 57(3):407–423. https://doi.org/10.1080/02626667.2012.658401
2. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1–4):28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013
3. Aksoy H, Unal NE, Eris E, Yuce MI (2013) Stochastic modeling of Lake Van water level time series with jumps and multiple trends. Hydrol Earth Syst Sci 17(6):2297–2303. https://doi.org/10.5194/hess-17-2297-2013
4. Altunkaynak A (2007) Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resour Manag 21(2):399–408. https://doi.org/10.1007/s11269-006-9022-6
5. Altunkaynak A (2014) Predicting water level fluctuations in Lake Michigan-Huron using wavelet-expert system methods. Water Resour Manag 28(8):2293–2314. https://doi.org/10.1007/s11269-014-0616-0
6. Altunkaynak A, Şen Z (2007) Fuzzy logic model of lake water level fluctuations in Lake Van, Turkey. Theor Appl Climatol 90(3–4):227–233. https://doi.org/10.1007/s00704-006-0267-z
7. Altunkaynak A, Özger M, Sen Z (2003) Triple diagram model of level fluctuations in Lake Van, Turkey. Hydrol Earth Syst Sci 7(2):235–244. https://doi.org/10.5194/hess-7-235-2003
8. Armstrong JS, Fildes R (1995) Correspondence on the selection of error measures for comparisons among forecasting methods. J Forecast 14(1):67–71. https://doi.org/10.1002/for.3980140106
9. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346(8983):1135–1138. https://doi.org/10.1016/S0140-6736(95)91804-3
10. Box GE, Jenkins GM, Reinsel GC (2011) Time series analysis: forecasting and control, 4th edn. Wiley, Hoboken. https://doi.org/10.1002/9781118619193
11. Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540. https://doi.org/10.1109/TCOM.1983.1095851
12. Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28(13):4747–4763. https://doi.org/10.1007/s11269-014-0773-1
13. Cadenas E, Rivera W (2009) Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renew Energy 34(1):274–278. https://doi.org/10.1016/j.renene.2008.03.014
14. Chattopadhyay S (2007) Feed forward artificial neural network model to predict the average summer-monsoon rainfall in India. Acta Geophys 55(3):369–382. https://doi.org/10.2478/s11600-007-0020-8
15. Chaturvedi DK, Sinha AP, Malik OP (2015) Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Int J Electr Power Energy Syst 67:230–237. https://doi.org/10.1016/j.ijepes.2014.11.027
16. Çimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378(3):253–262. https://doi.org/10.1016/j.jhydrol.2009.09.029
17. Coops H, Beklioglu M, Crisman TL (2003) The role of water-level fluctuations in shallow lake ecosystems—workshop conclusions. Hydrobiologia 506:23–27. https://doi.org/10.1023/B:HYDR.0000008595.14393.77
18. Coulibaly P (2010) Reservoir computing approach to Great Lakes water level forecasting. J Hydrol 381(1):76–88. https://doi.org/10.1016/j.jhydrol.2009.11.027
19. Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a):427–431. https://doi.org/10.1080/01621459.1979.10482531
20. Faust O, Acharya UR, AdeliH Adeli A (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56–64. https://doi.org/10.1016/j.seizure.2015.01.012
21. Furey PC, Nordin RN, Mazumder A (2004) Water level drawdown affects physical and biogeochemical properties of littoral sediments of a reservoir and a natural lake. Lake Reserv Manag 20(4):280–295. https://doi.org/10.1080/07438140409354158
22. Güldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir Lake level forecasting. Water Resour Manag 24(1):105–128. https://doi.org/10.1007/s11269-009-9439-9
23. Håkanson L (1977) Influence of wind, fetch, and water depth on distribution of sediments in lake Vanern, Sweden. Can J Earth Sci 14:397–412. https://doi.org/10.1139/e77-040
24. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
25. Hynes HBN (1961) The effects of water-level fluctuations on littoral fauna. Verhandlungender Int Ver Theor Angew Limnol 14(2):652–656
26. Imani M, You RJ, Kuo CY (2014) Forecasting Caspian Sea level changes using satellite altimetry data (June 1992–December 2013) based on evolutionary support vector regression algorithms and gene expression programming. Global Planet Change 121:53–63. https://doi.org/10.1016/j.gloplacha.2014.07.002
27. Jin J, Shi J (2001) Automatic feature extraction of waveform signals for in-process diagnostic performance improvement. J Intell Manuf 12(3):257–268. https://doi.org/10.1023/A:1011248925750
28. Joo TW, Kim SB (2015) Time series forecasting based on wavelet filtering. Expert Syst Appl 42(8):3868–3874. https://doi.org/10.1016/j.eswa.2015.01.026
29. Jurasz J, Mikulik J (2016) Day ahead electric power load forecasting by WT-ANN. PrzeglądElektrotechniczny 92:152–154. https://doi.org/10.15199/48.2016.04.32
30. Kasashima N, Mori K, Ruiz GH, Taniguchi N (1995) Online failure detection in face milling using discrete wavelet transform. CIRP Ann Manuf Technol 44(1):483–487. https://doi.org/10.1016/S0007-8506(07)62368-3
31. Khatibi R, Ghorbani MA, Naghipour L, Jothiprakash V, Fathima TA, Fazelifard MH (2014) Inter-comparison of time series models of lake levels predicted by several modeling strategies. J Hydrol 511:530–545. https://doi.org/10.1016/j.jhydrol.2014.01.009
32. Khwaja AS, Naeem M, Anpalagan A, Venetsanopoulos A, Venkatesh B (2015) Improved short-term load forecasting using bagged neural networks. Electr Power Syst Res 125:109–115. https://doi.org/10.1016/j.epsr.2015.03.027
33. Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180. https://doi.org/10.1016/j.cageo.2011.08.027
34. Kisi O, Shiri J, Karimi S, Shamshirband S, Motamedi S, Petković D, Hashim R (2015) A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl Math Comput 270:731–743. https://doi.org/10.1016/j.amc.2015.08.085
35. Kundzewicz ZW (2011) Climate changes, their reasons and effects—observations and projections. Landf Anal 15:39–49
36. Lafrenière M, Sharp M (2003) Wavelet analysis of inter-annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta. Hydrol Process 17(6):1093–1118. https://doi.org/10.1002/hyp.1187
37. Lan Y (2014) Forecasting performance of support vector machine for the Poyang Lake’s water level. Water Sci Technol 70(9):1488–1495. https://doi.org/10.2166/wst.2014.396
38. Loiselle S, Bracchini AL, Cozar A, Dattilo AM, Rossi C (2005) Extensive spatial analysis of the light environment in a subtropical shallow lake, Laguna Ibera, Argentina. Hydrobiologia 534:181–191. https://doi.org/10.1007/s10750-004-1504-z
39. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. https://doi.org/10.1109/34.192463
40. Markiewicz J (2005) Informacje o jakości jezior połozonych w zlewni rzeki Gasawki na podstawie badań prowadzonych w 2004 roku. WIOŚ, Bydgoszcz
41. Marshall B, Maes M (1995) The enhancement of fisheries in small water bodies. In: Small water bodies and their fisheries in Southern Africa. Committee for Inland Fisheries of Africa Technical Paper No. 29. FAO, Rome, pp 44–46. http://www.fao.org/docrep/008/v5345e/v5345e00.htm. Seen 9 May 2016
42. McCarthy E (2013) Shipping, fisheries and recreation all suffer from continuing low lake levels. Medill Reports. Medill News Service, Chicago, IL. http://newsarchive.medill.northwestern.edu/chicago/news-226248.html. Seen 9 May 2016
43. Mellit A, Pavan AM (2010) A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol Energy 84(5):807–821. https://doi.org/10.1016/j.solener.2010.02.006
44. Nalley D, Adamowski J, Khalil B, Ozga-Zielinski B (2013) Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967–2006 using the discrete wavelet transform. Atmos Res 132–133:375–398. https://doi.org/10.1016/j.atmosres.2013.06.011CrossRefGoogle Scholar
45. National Oceanic and Atmospheric Administration (NOAA) (2016) A new generation of water planners confronts change along the Colorado River. U.S. Climate Resilience Toolkit. NOAA, Asheville, NC. https://toolkit.climate.gov/taking-action/new-generation-water-planners-confronts-change-along-colorado-river. Seen 9 May 2016
46. Niewiarowski W (1995) Main features of the present geographical environment in the Biskupin area. In: Niewiarowski W (ed) Outline of changes of the geographical environment in the Biskupin surroundings under influence of natural and anthropogenic factors during the Lateglacial and Holocene. Turpress, Toruń, pp 215–235
47. Noury M, Sedghi H, Babazedeh H, Fahmi H (2014) Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network. Water Resour 41(3):261–269. https://doi.org/10.1134/S0097807814030129
48. Nowlin WH, Davies JM, Nordin RN, Mazumder A (2004) Effects of water level fluctuation and short-term climate variation on thermal and stratification regimes of a British Columbia Reservoir and Lake. Lake Reserv Manag 20:91–109. https://doi.org/10.1080/07438140409354354
49. Ondimu S, Murase H (2007) Reservoir level forecasting using neural networks: lake Naivasha. Biosyst Eng 96(1):135–138. https://doi.org/10.1016/j.biosystemseng.2006.09.003
50. Piasecki A, Marszelewski W (2014) Dynamics and consequences of water levels fluctuations of selected lakes in the catchment of the Ostrowo-Gopło Channel. Limnol Rev 14(4):187–194. https://doi.org/10.1515/limre-2015-0009
51. Piasecki A, Jurasz J, Marszelewski W (2016) Application of multilayer perceptron artificial neural networks to mid-term water consumption forecasting—a case study. Ochr Środowiska 38(2):17–22
52. Rajwa-Kuligiewicz A, Bialik RJ, Rowiński PM (2016) Wavelet characteristics of hydrological and dissolved oxygen time series in a lowland river. Acta Geophys 64(3):649–669. https://doi.org/10.1515/acgeo-2016-0023
53. Samui P, Kim D (2014) Applicability of artificial intelligence to reservoir induced earthquakes. Acta Geophys 62(3):608–619. https://doi.org/10.2478/s11600-014-0201-1
54. Sanikhani H, Kisi O, Kiafar H, Ghavidel SZ (2015) Comparison of different data-driven approaches for modeling lake level fluctuations: the case of Manyas and Tuz Lakes (Turkey). Water Resour Manag 29(5):1557–1574. https://doi.org/10.1007/s11269-014-0894-6
55. Shafaei M, Kisi O (2015) Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resour Manag 30(1):1–19. https://doi.org/10.1007/s11269-015-1147-z
56. Shanno DF (1985) On Broyden-Fletcher-Goldfarb-Shanno method. J Optim Theory Appl 46(1):87–94
57. Silvestri S (2010) Small lakes management on vancouver Island. BC Lake Stewartship Society 2010 Community Forum Presentations. BCLSS, Coquitlam, BC. http://www.bclss.org/library/library/doc_download/224-introduction-to-small-lake-fisheries-management-on-vancouver-island-scott-silvestri.html. Seen 9 May 2016
58. Smyczyńska U, Smyczyńska J, Tadeusiewicz R (2015) Neural modelling of growth hormone therapy for the prediction of therapy results. Bio-Algorithms Med-Syst 11(1):33–45. https://doi.org/10.1515/bams-2014-0021
59. Sundararajan D (2015) Discrete wavelet transform: a signal processing approach. Wiley, Singapore
60. Szoplik J (2015) Forecasting of natural gas consumption with artificial neural networks. Energy 85:208–220. https://doi.org/10.1016/j.energy.2015.03.084
61. Tezel G, Büyükyıldız M, Kahramanlı H (2013) Lake level prediction using artificial neural network with adaptive activation function. In: Camarinhas CL, Zaharia R, Dan D, Lucaci G, Batisha A, Arad V (eds) Recent advances in civil and mining engineering. WSEAS Press, Greece, pp 309–313
62. Üneş F, Demirci M, Kişi Ö (2015) Prediction of Millers Ferry Dam Reservoir level in USA using artificial neural network. Period Polytech Civ Eng 59(3):309–318. https://doi.org/10.3311/PPci.7379
63. Walton B (2010) Low water may halt Hoover Dam’s power. Circle of Blue, Traverse City, MI. http://www.circleofblue.org/2010/world/low-water-may-still-hoover-dam%E2%80%99s-power/. Seen 9 May 2016
64. Wiszniowski J, Plesiewicz BM, Trojanowski J (2014) Application of real time recurrent neural network for detection of small natural earthquakes in Poland. Acta Geophys 62(3):469–485. https://doi.org/10.2478/s11600-013-0140-2
65. Young CC, Liu WC, Hsieh WL (2015) Predicting the water level fluctuation in an alpine lake using physically based, artificial neural network, and time series forecasting models. Math Probl Eng 2005:708204. https://doi.org/10.1155/2l015/708204

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Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-b618ce13-e803-4c05-8b3b-18d54a26f794