Application of machine learning ensemble models for rainfall prediction

Ahmadi, Hasan; Aminnejad, Babak; Sabatsany, Hojat

doi:10.1007/s11600-022-00952-y

Artykuł - szczegóły

Tytuł artykułu

Application of machine learning ensemble models for rainfall prediction

Autorzy

Ahmadi Hasan , Aminnejad Babak , Sabatsany Hojat

Wybrane pełne teksty z tego czasopisma

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

Identyfikatory

DOI

10.1007/s11600-022-00952-y

Warianty tytułu

Języki publikacji

Abstrakty

Practical information can be drawn from rainfall for making long-term water resources management plans, taking flood mitigation measures, and even establishing proper irrigation systems. Given that a large amount of data with high resolution is required for physical modeling, this study proposes a new standalone sequential minimal optimization (SMO) regression model and develops its ensembles using Dagging (DA), random committee (RC), and additive regression (AR) models (i.e., DA-SMO, RC-SMO, and AR-SMO) for rainfall prediction. First, 30-year monthly data derived from the year 1988 to 2018 including evaporation, maximum and minimum temperatures, maximum and minimum relative humidity rates, sunshine hours, and wind speed as input and rainfall as the output were acquired from a synoptic station in Kermanshah, Iran. Next, based on the Pearson correlation coefficient (r-value) between input and output variables, different input scenarios were formed. Then, the dataset was separated into three subsets: development (1988–2008), calibration (2009–2010), and validation (2011–2018). Finally, the performance of the developed algorithms was validated using different visual (scatterplot and boxplot) and quantitative (percentage of BIAS, root mean square error, Nash–Sutcliffe efficiency, and mean absolute error) metrics. The results revealed that minimum relative humidity had the greatest effect on rainfall prediction. The most effective input scenario featured all the input variables except for wind speed. Our findings indicated that the DA-SMO ensemble algorithm outperformed other algorithms.

Słowa kluczowe

Dagging ensemble models machine learning performance evaluation rainfall prediction SMO

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2023

Tom

Vol. 71, no. 4

Strony

1775--1786

Opis fizyczny

Bibliogr. 71 poz., rys., tab.

Twórcy

autor

Ahmadi Hasan

hgahmadi@riau.ac.ir

Department of Civil Engineering, Islamic Azad University Roudehen Branch, Tehran, Iran

autor

Aminnejad Babak

Department of Civil Engineering, Islamic Azad University Roudehen Branch, Tehran, Iran

autor

Sabatsany Hojat

Department of Civil Engineering, Islamic Azad University Roudehen Branch, Tehran, Iran

Bibliografia

1. Adefisan E (2018) Climate change impact on rainfall and temperaturę distributions over West Africa from three IPCC scenarios. J Earth Sci Clim Change 9:476
2. Ahmadi A, Han D, Kakaei Lafdani E, Moridi A (2015) Input selection for long-lead precipitation prediction using large-scale climate variables: a case study. J Hydroinf 17(1):114–129
3. Aksoy H, Dahamsheh A (2009) Artificial neural network models for forecasting monthly precipitation in Jordan. Stoch Env Res Risk Assess 23(7):917–931
4. Atiquzzaman M, Kandasamy J (2018) Robustness of Extreme Learning Machine in the prediction of hydrological flow series. Comput Geosci 120:105–114
5. Bui DT, Khosravi K, Tiefenbacher J, Nguyen H, Kazakis N (2020) Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Sci Total Environ 721:137612
6. Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinf 12(4):458–473
7. Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Ahmad BB (2020) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol 583:124602
8. Chen Z, Liu Z, Yin L, Zheng W (2022) Statistical analysis of regional air temperature characteristics before and after dam construction. Urban Clim. https://doi.org/10.1016/j.uclim.2022.101085
9. Cui X, Penh H, Wen S, Zhi L (2010) Component selection in the additive regression model. Scand J Stat 40(3):491–510
10. Delleur JW, Kavvas ML (1978) Stochastic models for monthly rainfall forecasting and synthetic generation. J Appl Meteorol 17(10):1528–1536
11. Ebrahimi M, Rostami H, Osouli A, Rosanna Saindon RG (2022) Use of Geoelectrical Techniques to Detect Hydrocarbon Plume in Leaking Pipelines, ASCE Lifelines Conference 2021-2022, Los Angeles
12. Elbaz K, Shen S, Sun W, Yin Z, Zhou A (2020) Incorporating improved particle swarm optimization into ANFIS. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2974058
13. Elbazz K, Shen S, Zhou A, Yuan D, Xu Y (2019) Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm. Appl Sci 9(4):780. https://doi.org/10.3390/app9040780
14. Gao Q-Q, Bai Y-Q, Zhan Y-R (2019) Quadratic kernel-free least square twin support vector machine for binary classification problems. J Oper Res Soc China 7:539–559
15. Ghiasi-Freez J, Kadkhodaie-Ilkhchi A, Ziaii M (2012) Improving the accuracy of flow units prediction through two committee machine models: An example from the South Pars Gas Field, Persian Gulf Basin, Iran. Comput Geosci 46:10–23
16. Ghumman AR, Ghazaw YM, Sohail AR, Watanabe K (2011) Runoff forecasting by artificial neural network and conventional model. Alexandria Eng J 50(4):345–350
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 171:21–30
18. Hashmi S, Halawani MO, AmirAhmad MB (2015) Model trees and sequential minimal optimization based support vector machine models for estimating minimum surface roughness value. Appl Math Model 39(3):1119–1136
19. Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu AX, Chen W, Ahmad BB (2018) Landslide susceptibility mapping using J48 decision tree with adaboost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA 163:399–413
20. Jiang S, Zuo Y, Yang M, Feng R (2021) Reconstruction of the Cenozoic tectono-thermal history of the dongpu depression, bohai bay basin, China: constraints from apatite fission track and vitrinite reflectance data. J Petrol Sci Eng 205:108809. https://doi.org/10.1016/j.petrol.2021.108809
21. Kadam AK, Wagh VM, Muley AA, Umrikar BN, Sankhua RN (2019) Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India. Model Earth Syst Environ 5:951–962
22. Khosravi K, Barzegar R, Miraki S, Adamowski J, Daggupati P, Alizadeh MR, Pham B, Alami M (2020b) Stochastic modeling of groundwater fluoride contamination: introducing lazy learners. In press, Groundwater
23. Khosravi K, Golkarian A, Booij M, Barzegar R, Sun W, Yaseen ZM, Mosavi A (2021) Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms. Hydrol Sci J 66:1457–1474. https://doi.org/10.1080/02626667.2021.1928673
24. Khosravi K, Golkarian A, Barzegar R, Aalami MT, Heddam S, Omidvar E, Keestra S, Opez-Vicente M (2022a) Multi-step-ahead soil temperature forecasting at multiple-depth based on meteorological data: integrating resampling algorithms and machine learning models. Under press, Pedosphere
25. Khosravi K, Golkarian A, Melesse A, Deo R (2022b) Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach. J Hydrol 610:127963. https://doi.org/10.1016/j.jhydrol.2022.127963
26. Khosravi K, Cooper, J. R., Daggupati, P., Pham, B. T., & Bui, D. T. (2020b). Bedload transport rate prediction: application of novel hybrid data mining techniques. Journal of Hydrology, 124774.
27. Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour Manage 25(13):3135–3152
28. Legates DR, Mccabe GJ (1999) Evaluating the use of „goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241
29. Liu Y, Zhang K, Li Z, Liu Z, Wang J, Huang P (2020) A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds. J Hydrol (amsterdam) 590:125440. https://doi.org/10.1016/j.jhydrol.2020.125440
30. Liu B, Spiekermann R, Zhao C, Püttmann W, Sun Y, Jasper A, Uhl D (2022a) Evidence for the repeated occurrence of wildfires in an upper Pliocene lignite deposit from Yunnan, SW China. Int J Coal Geol 250:103924. https://doi.org/10.1016/j.coal.2021.103924
31. Liu S, Liu Y, Wang C, Dang X (2022b) The Distribution characteristics and human health risks of high-fluorine groundwater in coastal plain: a case study in Southern Laizhou Bay. Frontiers in Environmental Science, China. https://doi.org/10.3389/fenvs.2022b.901637
32. Luk KC, Ball JE, Sharma A (2000) A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol 227(1–4):56–65
33. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007a) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900
34. Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith TL (2007b) Model evaluation guidelines for systematic quantification of accuracy in watershedsimulations. Trans ASABE 50:885–900. https://doi.org/10.13031/2013.23153
35. Nguyen H, Mehrabi M, Kalantar B, Moayedi H, Abdullahi MM (2019) Potential of hybrid evolutionary approaches for assessment of geohazard landslide susceptibility mapping. Geomat Natural Hazard Risk 10(1):1667–1693
36. Nhu V-H, Khosravi K, Cooper JR, Karimi M, Kisi O, Pham BT, Lyu Z (2020) Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method. Hydrol Sci J 65(12):2116–2127
37. Niranjan A, Haripriya DK, Pooja R, Sarah S, Deepa Shenoy P, Venugopal KR (2018) EKRV: Ensemble of kNN and Random Committee Using Voting for Efficient Classification of Phishing. In: Advances in Intelligent Systems and Computing, vol 713, pp 403–414
38. Oyebode O, Stretch D (2019) Neural network modelling of hydrological systems: a review of implementation techniques. In: Natural resource modelling. Wiley, pp 1–14. https://doi.org/10.1002/nrm.12189
39. Osouli A, Ebrahimi M, Alzamora D, Shoup HZ, Pagenkopf J (2022) Multi-criteria assessment of bridge sites for conducting PSTD/ISTD: case histories. Transp Res Rec J Transp Res Board. https://doi.org/10.1177/03611981221108153
40. Pham BT, Le LM, Le T-H, Thi Bui K-T, Minh V, Prakhsh I (2020) Development of advanced artificial intelligence models for daily rainfall prediction. Atmos Res 237:104845
41. Quan Q, Liang W, Yan D, Lei J (2022) Influences of joint action of natural and social factors on atmospheric process of hydrological cycle in Inner Mongolia. China Urban Clim 41:101043. https://doi.org/10.1016/j.uclim.2021.101043
42. Ridwan WM, Sapitang M, Aziz A, Kushiar KF, Ahmed AN, El-Shafie A (2021) Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia. Ain Shams Eng J 12(2):1651–1663
43.Samantaray S, Tripathy O, Sahoo A, & Ghose DK, (2020). Rainfall forecasting through ANN and SVM in Bolangir Watershed, India. In: smart intelligent computing and applications. Springer, Singapore (pp. 767–774)
44. Sánchez-Monedero J, Salcedo-Sanz S, Gutiérrez PA, Casanova-Mateo C, Hervás-Martínez C (2014) Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal–ordinal support vector classifier. Eng Appl Artif Intell 34:199–207. https://doi.org/10.1016/j.engappai.2014.05.016
45. Sheikh Khozani Z, Bonakdari H, Ebtehaj I (2017) An analysis of shear stress distribution in circular channels with sediment deposition based on Gene Expression Programming. Int J Sediment Res 32(4):575–584
46. Sivapragasam C, Liong SY, Pasha MFK (2001) Rainfall and runoff forecasting with SSA–SVM approach. J Hydroinf 3(3):141–152
47. Tian H, Qin Y, Niu Z, Wang L, Ge S (2021a) Summer maize mapping by compositing time series sentinel-1a imagery based on crop growth cycles. J Indian Soc Remote Sens 49(11):2863–2874. https://doi.org/10.1007/s12524-021-01428-0
48. Tian H, Wang Y, Chen T, Zhang L, Qin Y (2021b) Early-season mapping of winter crops using sentinel-2 optical imagery. Remote Sens (basel, Switzerland) 13(19):3822. https://doi.org/10.3390/rs13193822
49. Ting, KM, Witten IH, (1997) stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375
50. Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239(1–4):132–147
51. Wang D, Hagen SC, Alizad K (2013) Climate change impact and uncertainty analysis of extreme rainfall events in the Apalachicola River basin, Florida. J Hydrol 480:125–135
52. Wang S, Zhang K, Chao L, Li D, Tian X, Bao H, Xia Y (2021) Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. J Hydrol (amsterdam) 603:126964. https://doi.org/10.1016/j.jhydrol.2021.126964
53. Wang Y, Cheng H, Hu Q, Liu L, Jia L, Gao S, Wang Y (2022) Pore structure heterogeneity of Wufeng-Longmaxi shale, Sichuan Basin, China: evidence from gas physisorption and multifractal geometries. J Pet Sci Eng 208:109313. https://doi.org/10.1016/j.petrol.2021.109313
54. Witten IH, Frank E (2005) Data Mining: Practical Machine Learning Tools and Techniques. Second edn, p 558
55. Wu J, Liu M, Jin L (2010) A hybrid support vector regression approach for rainfall forecasting using particle swarm optimization and projection pursuit technology. Int J Comput Intell Appl 9(02):87–104
56. Xie W, Li X, Jian W, Yang Y, Liu H, Robledo LF, Nie W (2021a) A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: a case of Xiaojin County China. ISPRS Int J Geo-Inf 10(2):93. https://doi.org/10.3390/ijgi10020093
57. Xie W, Nie W, Saffari P, Robledo LF, Descote, P.,... Jian, W. (2021b) Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City. China Nat Hazard (dordrecht) 109(1):931–948. https://doi.org/10.1007/s11069-021-04862-y
58. Xu B, Lin B (2017) Does the high–tech industry consistently reduce CO 2 emissions? Results from nonparametric additive regression model. Environ Impact Assess Rev 63:44–58
59. Yaseen ZM, Ebtehaj I, Kim S, Sanikhani H, Asadi H, Ghareb MI, Shahid S (2019) Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water 11(3):502
60. Yevjevich V (1987) Stochastic models in hydrology. Stoch Hydrol Hydraul 1(1):17–36
61. Yin L, Wang L, Keim BD, Konsoer K, Zheng W (2022a) Wavelet analysis of dam injection and discharge in three gorges dam and reservoir with precipitation and river discharge. Water 14(4):567. https://doi.org/10.3390/w14040567
62. Yin L, Wang L, Zheng W, Ge L, Tian J, Liu, Y.,... Liu, S. (2022b) Evaluation of empirical atmospheric models using swarm-c satellite data. Atmosphere 13(2):294. https://doi.org/10.3390/atmos13020294
63. Zhang K, Ali A, Antonarakis A, Moghaddam M, Saatchi S, Tabatabaeenejad A, Moorcroft P (2019a) The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: an analysis using radar-derived estimates of root-zone soil moisture. J Geophys Res Biogeosci 124(11):3208–3231. https://doi.org/10.1029/2018JG004589
64. Zhang K, Wang S, Bao H, Zhao X (2019b) Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. Nat Hazard 19(1):93–105. https://doi.org/10.5194/nhess-19-93-2019
65. Zhang K, Shalehy MH, Ezaz GT, Chakraborty A, Mohib KM, Liu L (2022) An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis. Environ Model Softw 148:105279. https://doi.org/10.1016/j.envsoft.2021.105279
66. Zhao F, Song L, Peng Z, Yang J, Luan G, Chu C, Xie Z (2021a) Night-time light remote sensing mapping: construction and analysis of ethnic minority development index. Remote Sens (basel, Switzerland) 13(11):2129. https://doi.org/10.3390/rs13112129
67. Zhao F, Zhang S, Du Q, Ding J, Luan G, Xie Z (2021b) Assessment of the sustainable development of rural minority settlements based on multidimensional data and geographical detector method: a case study in Dehong China. Socio-Econ Plan Sci. 78:101066
68. Zhao X, Xia H, Pan L, Song H, Niu W, Wang R, Qin Y (2021c) Drought monitoring over yellow river basin from 2003–2019 using reconstructed modis land surface temperature in gogle earth engine. Remote Sens (basel, Switzerland) 13(18):3748. https://doi.org/10.3390/rs13183748
69. Zhu B, Zhong Q, Chen Y, Liao S, Li Z, Shi K, Sotelo MA (2022a) A novel reconstruction method for temperature distribution measurement based on ultrasonic tomography. IEEE Trans Ultrason Ferroelectr Freq Control. https://doi.org/10.1109/TUFFC.2022.3177469
70. Zhu Z, Zhu Z, Wu Y, Han J (2022b) A Prediction method of coal burst based on analytic hierarchy process and fuzzy comprehensive evaluation. Front Earth Sci (lausanne). https://doi.org/10.3389/feart.2021.834958
71. Zuo Y, Jiang S, Wu S, Xu W, Zhang J, Feng R, Santosh M (2020) Terrestrial heat flow and lithospheric thermal structure in the Chagan depression of the Yingen-Ejinaqi Basin, north central China. Basin Res 32(6):1328–1346. https://doi.org/10.1111/bre.12430

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-9b582f21-3e0a-4b49-9593-b10e100f16c5