Applicability of machine learning techniques for multi-time step ahead runof forecasting

Bajirao, Tarate Suryakant; Elbeltagi, Ahmed; Kumar, Manish; Pham, Quoc Bao

doi:10.1007/s11600-022-00749-z

Artykuł - szczegóły

Tytuł artykułu

Applicability of machine learning techniques for multi-time step ahead runof forecasting

Autorzy

Bajirao Tarate Suryakant , Elbeltagi Ahmed , Kumar Manish , Pham Quoc Bao

Wybrane pełne teksty z tego czasopisma

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

Identyfikatory

DOI

10.1007/s11600-022-00749-z

Warianty tytułu

Języki publikacji

Abstrakty

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t+1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R² ) values of 168.94 m³ /s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R² values were observed to be 169.72 m³ /s, 0.67, 0.84, 0.7023 and 148.55 m3 /s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis.

Słowa kluczowe

ANN SVM RF M5P water resource management performance indicators

ANN SVM RF M5P zarządzanie zasobami wodnymi wskaźniki efektywności

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 2

Strony

757--776

Opis fizyczny

Bibliogr. 58 poz.

Twórcy

autor

Bajirao Tarate Suryakant

Department of Soil Science and Agriculture Chemistry, School of Agriculture, Lovely Professional University, Phagwara 144411, India

autor

Elbeltagi Ahmed

Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt

autor

Kumar Manish

manishgbpuat@yahoo.com

College of Agricultural Engineering, Dr. R.P.C.A.U., Pusa, Bihar 848125, India

autor

Pham Quoc Bao

quoc_bao.pham@us.edu.pl

Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland

https://orcid.org/0000-0002-0468-5962

Bibliografia

1. Agarwal A, Mishra SK, Ram S, Singh JK (2006) Simulation of runoff and sediment yield using artificial neural networks. Biosyst Eng 94(4):597–613
2. Alizadeh MJ, Kavianpour MR, Kisi O, Nourani V (2017) A new approach for simulating and forecasting the rainfall-runoff process within the next two months. J Hydrol 548:588–597
3. Anusree K, Varghese KO (2016) Streamflow prediction of Karuvannur river basin using ANFIS, ANN and MNLR. Proc Technol 24:101–108
4. Aoulmi Y, Marouf N, Amireche M (2021) The assessment of artificial neural network rainfall-runoff models under different input meteorological parameters case study: Seybouse basin, Northeast Algeria. J Water Land Dev 50:38–47
5. Araghinejad S, Fayaz N, Hosseini-Moghari S (2018) Development of a hybrid data driven model for hydrological estimation. Water Resour Manag 32:3737–3750
6. Avand M, Moradi HR, Ramazanzadeh Lasboyee M (2021a) Spatial prediction of future flood risk: an approach to the effects of climate change. Geosciences 11:25. https://doi.org/10.3390/geosciences11010025
7. Avand M, Moradi H, Ramazanzadeh Lasboyee M (2021b) Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran. Adv Space Res 67(10):3169–3186. https://doi.org/10.1016/j.asr.2021.02.011
8. Bafitlhile TM, Li Z (2019) Applicability of support vector machine and artificial neural network for flood forecasting in humid, semi-humid and semi-arid basins in China. Water 11:85
9. Bajirao TS, Kumar P (2021a) Geospatial technology for prioritization of Koyna River basin of India based on soil erosion rates using different approaches. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13155-7
10. Bajirao TS, Kumar P (2021b) Effectiveness of heuristic approach for daily sediment flow prediction of Koyna river basin. J Soil Water Conserv 20(1):12–21
11. Bajirao TS, Kumar P, Kumar M, Elbeltagi A, Kuriqi A (2021a) Superiority of hybrid soft computing models in daily suspended sediment estimation in highly dynamic rivers. Sustainability 13:542. https://doi.org/10.3390/su13020542
12. Bajirao TS, Kumar P, Kumar M, Elbeltagi A, Kuriqi A (2021b) Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins. Theor Appl Climatol. https://doi.org/10.1007/s00704-021-03681-2
13. Breiman L (2001) Random forests. Mach Learn 45:5–32
14. Budu K (2014) Comparison of wavelet-based ANN and regression models for reservoir inflow forecasting. J Hydrol Eng 19:1385–1400
15. Chakravarti A, Joshi N, Panjiar H (2015) Rainfall-runoff analysis using artificial neural network. Indian J Sci Technol 8(14):1–7
16. Chen G, Long T, Xiong J, Bai Y (2017) Multiple random forests modelling for urban water consumption forecasting. Water Resour Manag 31:4715–4729
17. Contreras P, Orellana-Alvear J, Muñoz P, Bendix J, Célleri R (2021) Influence of random forest hyperparameterization on short-term runoff forecasting in an Andean mountain catchment. Atmosphere (basel). https://doi.org/10.3390/atmos12020238
18. Costache R, Roxana Țîncu R, Elkhrachy I, Pham QB, Popa MC, Diaconu DC, Avand M, Costache I, Arabameri A, Bui DT (2020) New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrol Sci J 65:2816–2837. https://doi.org/10.1080/02626667.2020.1842412
19. Elbeltagi A, Kumari N, Dharpure JK, Mokhtar A, Alsafadi K, Kumar M, Mehdinejadiani B, Ramezani Etedali H, Brouziyne Y, Towfiqul Islam ARM, Kuriqi A (2021) Prediction of combined terrestrial evapotranspiration index (Ctei) over large river basin based on machine learning approaches. Water (switzerland) 13:1–18. https://doi.org/10.3390/w13040547
20. Elsafi SH (2014) Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan. Alex Eng J 53:655–662
21. Genc O, Kisi O, Ardichoglu M (2014) Determination of mean velocity and discharge in natural streams using neuro-fuzzy and neural network approaches. Water Resour Manag 28:2387–2400
22. Ghumman AR, Ghazaw YM, Sohail AR, Watanabe K (2011) Runoff forecasting by artificial neural network and conventional model. Alex Eng J 50:345–350. https://doi.org/10.1016/j.aej.2012.01.005
23. Gong Y, Zhang Y, Lan S, Wang H (2016) A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water Resour Manag 30:375–391
24. Guldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir lake level forecasting. Water Resour Manag 24:105–128
25. Hamidi O, Poorolajal J, Sadeghifar M, Abbasi H, Maryanaji Z, Faridi HR, Tapak L (2015) A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theor Appl Climatol 119:723–731
26. Hassan M, Shamim MA, Sikandar A, Mehmood I, Ahmed I, Ashiq SZ, Khitab A (2015) Development of sediment load estimation models by using artificial neural networking techniques. Environ Monit Assess 187:686
27. Huiping J, Yaning C, Gonghuan F, Zhi L, Weili D, Qifei Z (2021) Adaptability of machine learning methods and hydrological models to discharge simulations in datasparse glaciated watersheds. J Arid Land 13(6):549–567. https://doi.org/10.1007/s40333-021-0066-5
28. Jingwen X, Junjang Z, Wanchang Z, Zhongda H, Zheng Z (2009) Mid-short-term daily runoff forecasting by anns and multiple process-based hydrological models. In: IEEE Access, pp 526–529
29. Kheirfam H, Kashtiban SM (2018) A regional suspended load yield estimation model for ungauged watersheds. Water Sci Eng 11(4):328–337. https://doi.org/10.1016/j.wse.2018.09.008
30. Kim M, Gilley JE (2008) Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput Electron Agric 64:268–275
31. Kisi O (2015) Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering. Water Resour Manag 29:5109–5127
32. Kisi O, Nia AM, Gosheh MG, Tajabadi MRJ, Ahmadi A (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 26:457–474
33. Kumar M, Kumari A, Kushwaha DP, Kumar P, Malik A, Ali R, Kuriqi A (2020) Estimation of daily stage-discharge relationship by using data-driven techniques of a Perennial River, India. Sustainability 12(19):7877
34. Lee KT, Hung W, Meng C (2008) Deterministic insight into ANN model performance for storm runoff simulation. Water Resour Manag 22:67–82
35. Liang Z, Tang T, Li B, Liu T, Wang J, Hu Y (2018) Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: case study of Danjiangkou Reservoir. Hydrol Res 49:1513–1527. https://doi.org/10.2166/nh.2017.085
36. Lohani AK, Goel NK, Bhatia KKS (2014) Improving real time flood forecasting using fuzzy inference system. J Hydrol 509:25–41
37. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290
38. Niknia N, Kardan MH, Banaei SM, Torabi PH, Omidinasab F, Arabi Yazdi A (2014) Application of gamma test and neuro-fuzzy models in uncertainty analysis for prediction of pipeline scouring depth. J Water Resour Prot 6:514–525
39. Nourani V, Komasi M (2013) A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. J Hydrol 490:41–55
40. Olyaie E, Banejad H, Chau K, Melesse AM (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ Monit Assess 187:189
41. Papacharalampous GA, Tyralis H (2018) Evaluation of random forests and Prophet for daily streamflow forecasting. Adv Geosci 45:201–208. https://doi.org/10.5194/adgeo-45-201-2018
42. Peng F, Wen J, Zhang Y, Jin J (2020) Monthly streamflow prediction based on random forest algorithm and phase space reconstruction theory. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1637/1/012091
43. Pham LT, Luo L, Finley A (2021) Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds. Hydrol Earth Syst Sci 25:2997–3015
44. Quinlan JR (1992) Learning with continuous classes. In: Adams S (ed) Proceedings of AI’92. World Scientific, Singapore, pp 343–348
45. Reddy BSN, Pramada SK, Roshni T (2021) Monthly surface runoff prediction using artificial intelligence: a study from a tropical climate river basin. J Earth Syst Sci 130:35. https://doi.org/10.1007/s12040-020-01508-8
46. Remesan R, Shamim MA, Han D (2008) Model data selection using gamma test for daily solar radiation estimation. Hydrol Process 22:4301–4309. https://doi.org/10.1016/j.asej.2020.09.011
47. Roy B, Singh MP, Kaloop MR, Kumar D, Hu J-W, Kumar R, Hwang W-S (2021) Data-driven approach for rainfall-runoff modelling using equilibrium optimizer coupled extreme learning machine and deep neural network. Appl Sci 11:6238. https://doi.org/10.3390/app11136238
48. Sahraei A, Chamorro A, Kraft P, Breuer L (2021) Application of machine learning models to predict maximum event water fractions in streamflow. Front Water 3:652100. https://doi.org/10.3389/frwa.2021.652100
49. Senaviratne GMMMA, Udawatta RP, Anderson SH, Baffaut C, Thompson A (2014) Use of fuzzy rainfall–runoff predictions for claypan watersheds with conservation buffers in Northeast Missouri. J Hydrol 517:1008–1018
50. Shiri J (2018) Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology. J Hydrol 561:737–750
51. Sihag P, Karimi SM, Angelaki A (2019) Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Appl Water Sci 9:129
52. Singh VK, Kumar D, Kashyap PS, Singh PK, Kumar A, Singh SK (2020) Modelling soil permeability using different data driven algorithms based on physical properties of soil. J Hydrol 580:124223
53. Singh B, Sihag P, Parsaie A, Angelaki A (2021) Comparative analysis of artificial intelligence techniques for the prediction of infiltration process. Geol Ecol Landsc 5(2):109–118
54. Slieman AA, Dmitry K (2021) A comparative study between artificial neural networks and fuzzy inference system for estimation and filling of missing runoff data at Al-Jawadiyah Station. E3S Web Conf 264:01048. https://doi.org/10.1051/e3sconf/202126401048
55. Talebizadeh M, Morid S, Ayyoubzadeh SA, Ghasemzadeh M (2010) Uncertainty analysis in sediment load modeling using ANN and SWAT Model. Water Resour Manag 24:1747–1761
56. Tyralis H, Papacharalampous G, Langousis A (2019) A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water 11(5):910
57. Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780
58. Yariyan P, Avand M, Abbaspour RA, Haghighi AT, Costache R, Janizadeh OGS, Blaschke T (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat Nat Hazards Risk 11(1):2282–2314. https://doi.org/10.1080/19475705.2020.1836036

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-39d9bbda-ddd2-4e3f-b0da-04ff36038d08