Drought prediction using ensemble models

• Autorzy: Ehteram, Mohammad , Achite, Mohammed , Sheikh Khozani, Zohreh , Farrokhi, Alireza
• Języki publikacji: EN

Abstrakty

EN

Managing water resources requires the prediction of droughts. A robust model should be used for drought prediction as it is a complex and nonlinear problem. We used inclusive multiple models (IMM) and optimized radial basis function (RBF) neural networks to predict the standard precipitation index (SPI). The RBF model was trained using the coot optimization algorithm (COOA), salp swarm algorithm (SSA), shark algorithm (SA), and particle swarm optimization (PSO). Next, the outputs of RBF-COOA, RBF-SSA, RBF-SA, RBF-PSO, and RBF models were inserted into the RBF model. In the Wadi Ouahrane basin (WOB), these models were used to predict 1-month SPI (SPI-1), 3-month SPI (SPI-3), 6-month SPI (SPI-6), and 9-month SPI (SPI-9). The best input combinations were determined using a hybrid gamma test. Lagged SPI values were used to predict outputs. For predicting SPI-9, the Nash-Sutcliffe efficiency values (NSE) of the IMM, RBF-COOA, RBF-SSA, RBF-SA, RBF-PSO, and RBF models were 0.94, 0, 93, 0.91, 0.88, 0.80, and 0.75, respectively. The inclusive multiple models outperformed the other models in predicting SPIs-3 and 6. The ensemble models showed high potential for predicting SPI.

Słowa kluczowe

PL

prognozowanie suszy; algorytmy optymalizacji

EN

drought forecasting; optimization algorithms; ensemble models; radial basis function neural network models

• Czasopismo: Acta Geophysica
• Rocznik: 2024
• Tom: Vol. 72, no. 2
• Strony: 945--982
• Opis fizyczny: Bibliogr. 44 poz.

Twórcy

autor

Ehteram, Mohammad

• Department of Water Engineering, Semnan University, Semnan, Iran,

autor

Achite, Mohammed

• Water and Environment Laboratory, Hassiba Benbouali, University of Chlef, B.P. 78C, 02180 Ouled Fares, Chlef, Algeria
• National Higher School of Agronomy, ENSA, 16200 Hassan Badi, El Harrach, Algiers, Algeria

autor

Sheikh Khozani, Zohreh

• Institute of Structural Mechanics, Bauhaus Universität Weimar, 99423 Weimar, Germany

autor

Farrokhi, Alireza

• Department of Water Engineering, Semnan University, Semnan, Iran

Bibliografia

• 1. Abbaszadeh M, Ehteram M, Ahmed AN, Singh VP, Elshafie A (2021) The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization. Earth Sci Inf 14(4):2049-2075
• 2. Abedinia O, Amjady N, Ghasemi A (2016) A new metaheuristic algorithm based on shark smell optimization. Complexity. https://doi. org/10.1002/cplx.21634
• 3. Achite M, Banadkooki FB, Ehteram M, Bouharira A, Ahmed AN, Elshafie A (2022) Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts. Stoch Environ Res Risk Assess 36:1-26
• 4. Achour K, Meddi M, Zeroual A, Bouabdelli S, Maccioni P, Moramarco T (2020) Spatio-temporal analysis and forecasting of drought in the plains of northwestern Algeria using the standardized precipitation index. J Earth Syst Sci. https://doi.org/10.1007/ s12040-019-1306-3
• 5. Alsubih M, Mallick J, Talukdar S, Salam R, AlQadhi S, Fattah MA, Thanh NV (2021) An investigation of the short-term meteorological drought variability over Asir Region of Saudi Arabia. Theor Appl Climatol. https://doi.org/10.1007/ s00704-021-03647-4
• 6. Banadkooki FB, Singh VP, Ehteram M (2021) Multi-timescale drought prediction using new hybrid artificial neural network models. Nat Hazards. https://doi.org/10.1007/s11069-021-04550-x
• 7. Barbosa JHS, Fernandes A, Lima A, Assis L (2019) The influence of spatial discretization on HEC-HMS modelling: a case study. Int J Hydrol 3:442-449
• 8. Barua S, Ng AWM, Perera BJC (2012) Artificial neural network-based drought forecasting using a nonlinear aggregated drought index. J Hydrol Eng. https://doi.org/10.1061/(asce)he.1943-5584.0000574
• 9. Balakin EE, Ekmekcioglu Ö, Özger M (2021) Drought prediction using hybrid soft-computing methods for semi-arid region. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-01010-6
• 10. Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Longterm SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural networks and wavelet support vector regression models. J Hydrol. https://doi.org/10.1016/j.jhydrol.2013.10. 052
• 11. Belayneh A, Adamowski J, Khalil B, Quilty J (2016) Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmos Res. https://doi.org/10.1016/j.atmosres.2015.12.017
• 12. Cui F, Al-Sudani ZA, Hassan GS, Afan HA, Ahammed SJ, Yaseen ZM (2022) Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: comparative study and insight for federated learning technology. J Hydrol 606:127384
• 13. Dehghanipour MH, Karami H, Ghazvinian H, Kalantari Z, Dehgha-nipour AH (2021) Two comprehensive and practical methods for simulating pan evaporation under different climatic conditions in Iran. Water 13(20):2814
• 14. Deo RC, §ahin M (2015) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmos Res. https://doi.org/10.1016/j.atmos res.2014.10.016
• 15. Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Environ Res Risk Assess. https://doi.org/ 10.1007/s00477-016-1265-z
• 16. Jalalkamali A, Moradi M, Moradi N (2015) Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-014-0717-6
• 17. Jamei M, Elbeltagi A, Maroufpoor S, Karbasi M, Jamei M, Jalali M, Najafzadeh N (2022) Combined Terrestrial Evapotranspiration Index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection. Comput Electron Agric 193:106687
• 18. Khan MMH, Muhammad NS, El-Shafie A (2020) Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125380
• 19. Khatibi R, Nadiri AA (2021) Inclusive multiple models (IMM) for predicting groundwater levels and treating heterogeneity. Geosci Front 12(2):713-724
• 20. Kisi O, Docheshmeh Gorgij A, Zounemat-Kermani M, Mahdavi-Mey-mand A, Kim S (2019) Drought forecasting using novel heuristic methods in a semi-arid environment. J Hydrol. https://doi.org/10. 1016/j.jhydrol.2019.124053
• 21. Kumar A, Kumar P, Singh VK (2019) Evaluating different machine learning models for runoff and suspended sediment simulation. Water Resour Manag. https://doi.org/10.1007/s11269-018-2178-z
• 22. Lotfirad M, Esmaeili-Gisavandani H, Adib A (2022) Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran. J Water Clim Change 13(2):383-406
• 23. Malik A, Kumar A, Kim S, Kashani MH, Karimi V, Sharafati A, Ghor-bani MA, Al-Ansari N, Salih SQ, Yaseen ZM, Chau KW (2020) Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model. Eng Appl Comput Fluid Mech. https://doi.org/10.1080/ 19942060.2020.1715845
• 24. Malik A, Tikhamarine Y, Souag-Gamane D, Rai P, Sammen SS, Kisi O (2021a) Support vector regression integrated with novel metaheuristic algorithms for meteorological drought prediction. Mete-orol Atmos Phys. https://doi.org/10.1007/s00703-021-00787-0
• 25. Malik A, Tikhamarine Y, Sammen SS, Abba SI, Shahid S (2021b) Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13445-0
• 26. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, vol 17, no 22, pp 179-183
• 27. Meydani A, Dehghanipour A, Schoups G, Tajrishy M (2022) Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: application to Urmia Lake basin, Iran. J Hydrol Reg Stud 44:101228
• 28. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw. https://doi.org/10. 1016/j.advengsoft.2017.07.002
• 29. Mohamadi S, Sammen SS, Panahi F, Ehteram M, Kisi O, Mosavi A, Ahmed AN, El-Shafie A, Al-Ansari N (2020) Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm. Nat Hazards. https:// doi.org/10.1007/s11069-020-04180-9
• 30. Mokhtarzad M, Eskandari F, Jamshidi Vanjani N, Arabasadi A (2017) Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environ Earth Sci. https://doi.org/10.1007/ s12665-017-7064-0
• 31. Mossad A, Alazba AA (2018) Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems. Arab J Geosci. https://doi.org/10.1007/s12517-018-3487-5
• 32. Mouatadid S, Raj N, Deo RC, Adamowski JF (2018) Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region. Atmos Res. https://doi.org/10.1016/j.atmosres.2018. 05.012
• 33. Najafzadeh M, Ghaemi A, Emamgholizadeh S (2019) Prediction of water quality parameters using evolutionary computing-based formulations. Int J Environ Sci Technol. https://doi.org/10.1007/ s13762-018-2049-4
• 34. Naruei I, Keynia F (2021) A new optimization method based on COOT bird natural life model. Expert Syst Appl. https://doi.org/10. 1016/j.eswa.2021.115352
• 35. Omar LC, Mariano NC, Alberto G, Ernestina PG, Jorge MM, Enrique TD (2022) Sensitivity of four indices of meteorological drought for rainfed maize yield prediction in the State of Sinaloa, Mexico. Agriculture 12(4):525
• 36. Panahi F, Ehteram M, Ahmed AN, Huang YF, Mosavi A, El-Shafie A (2021) Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging. Ecol Ind 133:108285
• 37. Rahmati O, Panahi M, Kalantari Z, Soltani E, Falah F, Dayal KS, Mohammadi F, Deo RC, Tiefenbacher J, Tien Bui D (2020) Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134656
• 38. Rezaeianzadeh M, Stein A, Cox JP (2016) Drought forecasting using Markov chain model and artificial neural networks. Water Resour Manag. https://doi.org/10.1007/s11269-016-1283-0
• 39. Rose MA, Chithra NR (2022) Tree-based ensemble model prediction for hydrological drought in a tropical river basin of India. Int J Environ Sci Technol 20:1-18
• 40. Seifi A, Riahi H (2020) Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran. J Water Clim Change. https://doi.org/10.2166/wcc.2018. 003
• 41. Shirmohammadi B, Moradi H, Moosavi V, Semiromi MT, Zeinali A (2013) Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: Southeastern part of east Azerbaijan province, Iran). Nat Hazards. https://doi.org/10.1007/s11069-013-0716-9
• 42. Yadav A, Joshi D, Kumar V, Mohapatra H, Iwendi C, Gadekallu TR (2022) Capability and robustness of novel hybridized artificial intelligence technique for sediment yield modeling in Godavari River, India. Water 14(12):1917
• 43. Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S (2021) Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh. Sci Rep 11(1):1-25
• 44. Zhang X, Duan Y, Duan J, Chen L, Jian D, Lv M, Yang Q, Ma Z (2022) A daily drought index-based regional drought forecasting using the Global Forecast System model outputs over China. Atmos Res 273:106166

Identyfikatory

DOI

10.1007/s11600-023-01058-9