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Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection

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  • Malik, Anurag
  • Jamei, Mehdi
  • Ali, Mumtaz
  • Prasad, Ramendra
  • Karbasi, Masoud
  • Yaseen, Zaher Mundher

Abstract

Accurate ahead forecasting of reference evapotranspiration (ETo) is crucial for effective irrigation scheduling and management of water resources on a regional scale. A variety of methods are available for ETo simulation, but the most trending is complementary artificial intelligence (AI) paradigms. In this research, a novel Multivariate Variational Mode Decomposition technique (MVMD) integrated with the Ridge Regression (RR) feature selection algorithm and Kernel Extreme Learning Machine (KELM) model (i.e., MVMD-RR-KELM) was adopted to multi-step ahead (t + 3, and t + 7) forecasting of daily ETo in different climate of India. Here, the complementary expert system hybridized with the Boosted Regression Tree (BRT) and Extreme Gradient Boosted (XGBoost) along with the standalone counterpart models (KELM, BRT, and XGBoost) were examined to validate the robustness of the primary model. The complementary (i.e., MVMD-RR-KELM, MVMD-RR-BRT, & MVMD-RR-XGBoost) and their standalone counterpart models were trained and tested using daily climatic data of Hisar (located in Haryana State), Bathinda, and Ludhiana (located in Punjab State) sites. The forecasting accuracy of standalone and hybrid AI models was assessed using six goodness-of-fit metrics, i.e., R (Correlation Coefficient), RMSE (root mean square error), MAPE (mean absolute percentage error), NSE (Nash-Sutcliffe Efficiency), IA (Index of Agreement), U95% (Uncertainty Coefficient with 95% level) along with visual interpretation. According to the testing results, the hybrid MVMD-RR-KELM models had superior performance than other AI models for forecasting three and seven days ahead ETo. The KELM model optimized using the MVMD-RR technique provides promising and robust results with higher forecasting accuracy and minimum error for multi-step ahead forecasting of ETo in semi-arid and sub-humid regions.

Suggested Citation

  • Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:agiwat:v:272:y:2022:i:c:s0378377422003596
    DOI: 10.1016/j.agwat.2022.107812
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    1. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
    2. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    3. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
    4. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    5. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    6. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    7. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    8. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).
    9. Ahmadi, Farshad & Mehdizadeh, Saeid & Mohammadi, Babak & Pham, Quoc Bao & DOAN, Thi Ngoc Canh & Vo, Ngoc Duong, 2021. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 244(C).
    10. Pereira, Antonio Roberto & Pruitt, William Oregon, 2004. "Adaptation of the Thornthwaite scheme for estimating daily reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 66(3), pages 251-257, May.
    11. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    12. C.-Y. Xu & V. Singh, 2002. "Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(3), pages 197-219, June.
    13. Torres, Alfonso F. & Walker, Wynn R. & McKee, Mac, 2011. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data," Agricultural Water Management, Elsevier, vol. 98(4), pages 553-562, February.
    14. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2021. "Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine," Agricultural Water Management, Elsevier, vol. 243(C).
    15. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(C).
    16. Luo, Yufeng & Chang, Xiaomin & Peng, Shizhang & Khan, Shahbaz & Wang, Weiguang & Zheng, Qiang & Cai, Xueliang, 2014. "Short-term forecasting of daily reference evapotranspiration using the Hargreaves–Samani model and temperature forecasts," Agricultural Water Management, Elsevier, vol. 136(C), pages 42-51.
    17. Traore, Seydou & Wang, Yu-Min & Kerh, Tienfuan, 2010. "Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone," Agricultural Water Management, Elsevier, vol. 97(5), pages 707-714, May.
    18. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    19. Kisi, Ozgur, 2016. "Modeling reference evapotranspiration using three different heuristic regression approaches," Agricultural Water Management, Elsevier, vol. 169(C), pages 162-172.
    20. Masoud Karbasi, 2018. "Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1035-1052, February.
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    2. Zheng, Zihao & Ali, Mumtaz & Jamei, Mehdi & Xiang, Yong & Abdulla, Shahab & Yaseen, Zaher Mundher & Farooque, Aitazaz A., 2023. "Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    3. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    4. Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).

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