nach oben

Neural Computing and Applications

Erschienen in:

05.05.2018 | Original Article

Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models

verfasst von: Rasoul Mirabbasi, Ozgur Kisi, Hadi Sanikhani, Sarita Gajbhiye Meshram

Erschienen in: Neural Computing and Applications | Ausgabe 10/2019

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

This study investigates the performance of the M5Tree model, multivariate adaptive regression spline, least square support vector regression (LSSVR), gene expressing programming (GEP) and artificial neural networks methods in estimating monthly long-term rainfall. Data from 61 rain stations in Madhya Pradesh and Chhattisgarh states, Central India, were used in the applications. Geographical inputs and periodicity were used as inputs to the models, and the methods were compared with each other according to the determination coefficient (R²), mean absolute errors (MAE) and root-mean-square errors (RMSE). LSSVR was found to be the best model with RMSE = 13.93 mm, MAE = 9.52 mm and R² = 0.995 while the GEP provided the worst results with RMSE = 36.74 mm, MAE = 29.89 mm and R² = 0.955 in prediction of long-term rainfall in the test stage. The lowest RMSE (5.53 mm) and MAE (3.89 mm) were obtained for the Rajnandgaon Station, while the Raigarh Station provided the worst accuracy (RMSE = 31.8 mm and MAE = 21.56 mm) for LSSVR model.

Vorheriger Artikel Multiple-relations-constrained image classification with limited training samples via Pareto optimization

Nächster Artikel Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

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

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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

Abraham A, Steinberg D, Philip NS (2001) Rainfall forecasting using soft computing models and multivariate adaptive regression splines. IEEE SMC Trans 1:1–6

Adamowski J, Chan HF, Prasher SO, Sharda VN (2012) Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J Hydroinform 14(3):731–744CrossRef

Banzhaf W, Nordin P, Keller R, Francone F (1998) Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Co published by Morgan Kaufmann and Verlag, vol 340, 94104-3205

Bhagwat PP, Maity R (2012) Multistep-ahead river flow prediction using LS-SVR at daily scale. J Water Resour Protect 4(7):528–539CrossRef

Bhattacharya B, Solomatine DP (2005) Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing 63:381–396CrossRef

Bilgili M, Sahin M (2010) Prediction of long-term monthly temperature and rainfall in Turkey. Energy Sources Part A 32:60–71CrossRef

De Andrés J, Lorca P, de Cos Juez FJ, Sánchez-Lasheras F (2011) Bankruptcy forecasting: a hybrid approach using fuzzy c-means clustering and multivariate adaptive regression splines (MARS). Expert Syst Appl 38(3):1866–1875CrossRef

Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175CrossRef

Etemad-Shahidi A, Mahjoobi J (2009) Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Eng 36(15):1175–1181CrossRef

10.

Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence, vol 21. Springer, BerlinMATH

11.

Friedman JH (1991) Multivariate adaptive regression splines (with discussion). Ann Stat 19(1):79–141MathSciNet

12.

Friedman JH (1991b) Estimating functions of mixed ordinal and categorical variables using adaptive splines. Stanford Univ CA Lab for Computational Statistics

13.

Gajbhiye S, Meshram C, Mirabbasi R, Sharma SK (2016) Trend analysis of rainfall time series for Sindh River basin in India. Theor Appl Climatol 125:593–608CrossRef

14.

Gocić M, Motamedi S, Shamshirband S, Petković D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput Electron Agric 113:164–173CrossRef

15.

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(1):375–391CrossRef

16.

Han D, Chan L, Zhu N (2007) Flood forecasting using support vector machines. J Hydroinform 9(4):267–276CrossRef

17.

Hashim R, Roy C, Motamedi S, Shamshirband S, Petković D, Gocic M, Lee SC (2016) Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmos Res 171:21–30CrossRef

18.

Haykin S (1998) Neural networks—a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle RiverMATH

19.

Khalili K, Nazeri Tahoudi M, Mirabbasi R, Ahmadi F (2016) Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stoch Environ Res Risk Assess 30(4):1205–1221CrossRef

20.

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(14):5109–5127CrossRef

21.

Kisi O, Sanikhani H (2015) Modelling long-term monthly temperatures by several data-driven methods using geographical inputs. Int J Climatol 35(13):3834–3846CrossRef

22.

Kisi O, Sanikhani H (2015) Prediction of long-term monthly precipitation using several soft computing methods without climatic data. Int J Climatol 35(14):4139–4150CrossRef

23.

Kisi O, Shiri J, Tombul M (2013) Modeling rainfall–runoff process using soft computing techniques. Comput Geosci 51:108–117CrossRef

24.

Kisi O, Sanikhani H, Zounemat-Kermani M, Niazi F (2015) Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Comput Electron Agric 115:66–77CrossRef

25.

Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241CrossRef

26.

Mirabbasi R (2015) Application of artificial intelligence methods for groundwater quality prediction. In: Nadiri AA (ed) Application of artificial intelligence methods in geosciences and hydrology. OMICS Group eBooks, Foster City. ISBN 978-1-63278-061-4-04

27.

Moghaddamnia A, Ghafari M, Piri J, Han D (2009) Evaporation estimation using support vector machines technique. Int J Eng Appl Sci 5(7):415–423

28.

Mukherjee S, Vapnik V (1999) Support vector method for multivariate density estimation. In: Center for biological and computational learning. Department of Brain and Cognitive Sciences, MIT. C.B.C.L. no. 170

29.

Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290CrossRef

30.

Nourani V, Baghanam AH, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514:358–377CrossRef

31.

Özmen A, Batmaz İ, Weber GW (2014) Precipitation modeling by polyhedral RCMARS and comparison with MARS and CMARS. Environ Model Assess 19(5):425–435CrossRef

32.

Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrol Process 23(10):1437CrossRef

33.

Quinlan JR (1992) Learning with continuous classes. In: Proceedings of the fifth Australian joint conference on artificial intelligence, Hobart, Australia, 16–18 November. World Scientific, Singapore, pp 343–348

34.

Raghavendra NS, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386CrossRef

35.

Saghebian SM, Sattari MT, Mirabbasi R, Pal M (2014) Ground water quality classification by decision tree method in Ardebil region, Iran. Arab J Geosci 7(11):4767–4777CrossRef

36.

Sahraei S, Andalani SZ, Zakermoshfegh M, Sisakht BN, Talebbeydokhti N, Moradkhani H (2015) Daily discharge forecasting using least square support vector regression and regression tree. Sci Iran Trans Civ Eng 22(2):410–422

37.

Samui P (2011) Application of least square support vector machine (LSSVM) for determination of evaporation losses in reservoirs. Engineering 3(04):431CrossRef

38.

Sattari MT, Pal M, Apaydin H, Ozturk F (2013) M5 model tree application in daily river flow forecasting in Sohu stream, Turkey. Water Resour 40(3):233–242CrossRef

39.

Sattari MT, Pal M, Mirabbasi R, Abraham J (2018) Ensemble of M5 model tree based modelling of sodium adsorption ratio. J AI Data Min 6(1):69–78

40.

Saunders C, Stitson MO, Weston J, Bottou L, Schölkopf B, Smola A (1998) Support vector machine—reference manual. Royal Holloway technical report CSD-TR-98-03. Royal Holloway

41.

Shabri A, Suhartono (2012) Streamflow forecasting using least-squares support vector machines. Hydrol Sci J 57(7):1275–1293CrossRef

42.

Shamshirband S, Gocic M, Petkovic D, Saboohi H, Herawan T, Mat Kiah ML, Akib S (2015) Soft-computing methodologies for precipitation estimation: a case study. IEEE J Sel Top Appl Earth Observ Remote Sens 8(3):1353–1358

43.

Sharda VN, Prasher SO, Patel RM, Ojasvi PR, Prakash C (2008) Performance of multivariate adaptive regression splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data. Hydrol Sci J 53(6):1165–1175CrossRef

44.

Shenify M, Danesh AS, Gocić M, Taher RS, Abdul Wahab AW, Gani A, Shamshirband S, Petković D (2016) Precipitation estimation using support vector machine with discrete wavelet transform. Water Resour Manag 30(2):641–652CrossRef

45.

Shiri J, Marti P, Singh VP (2014) Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning. Hydrol Process 28(3):1215–1225CrossRef

46.

Singh KK, Pal M, Singh VP (2010) Estimation of mean annual flood in Indian catchments using backpropagation neural network and M5 model tree. Water Resour Manag 24(10):2007–2019CrossRef

47.

Smola AJ (1996) Regression estimation with support vector learning machines (Doctoral dissertation, Master’s thesis, Technische Universität München)

48.

Sotomayor KAL (2010) Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin. Hydrology Days, pp 58–68

49.

Sumi SM, Zaman M, Hirose H (2012) A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. Int J Appl Math Comput Sci 22(4):841–854MathSciNetMATHCrossRef

50.

Suykens J, De Brabanter J, De Moor B, Vandewalle JAK, Van Gestel T (2002) Least squares support vector machines, vol 4. World Scientific, SingaporeMATHCrossRef

51.

Swamee PK, Ojha CSP, Abbas A (1995) Mean annual flood estimation. J Water Resour Plan Manag 121(6):403–407CrossRef

52.

Teimoorzadeh K, Shiri J, Shiri AA (2015) Forecasting daily precipitation values, using wavelet conjunction models (case study: Tabriz and Marageh Stations, Iran). Science 34(4):265–269

53.

Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkMATHCrossRef

54.

Wang Y, Min J, Chen Y, Huang XY, Zeng M, Li X (2017) Improving precipitation forecast with hybrid 3DVar and time-lagged ensembles in a heavy rainfall event. Atmos Res 183:1–16CrossRef

55.

Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194CrossRef

56.

Willmott CJ (1984) On the evaluation of model performance in physical geography. Spatial statistics and models. Springer, Berlin, pp 443–460

57.

Zounemat-Kermani M, Kisi O, Rajaee T (2013) Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Appl Soft Comput 13(12):4633–4644CrossRef

Titel: Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models
verfasst von: Rasoul Mirabbasi
Ozgur Kisi
Hadi Sanikhani
Sarita Gajbhiye Meshram
Publikationsdatum: 05.05.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3519-9

Springer Professional

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 "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 10/2019

Optimization of energy management and conversion in the water systems based on evolutionary algorithms

A comparative analysis of ANN and chaotic approach-based wind speed prediction in India

Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering

Perturbation wavelet neural sliding mode position control for a voice coil motor driver

Modeling forces between the probe of atomic microscope and the scanning surface

Hopfield network-based approach to detect seam-carved images and identify tampered regions

Premium Partner