Abstract
Agricultural droughts are a prime concern for economies worldwide as they negatively impact the productivity of rain-fed crops, employment, and income per capita. In this study, Standard Precipitation Index (SPI) has been used to evaluate different drought indices for Rajasthan of India. In agricultural, hydrological, and meteorological applications such as irrigation scheduling, crop simulation, water budgeting, reservoir operations, and weather forecasting, the accurate estimation of the drought indices such as the Standardized Precipitation Index (SPI) plays an important role. Thus, the present study was conducted to examine the feasibility and effectiveness of the Random Subspace (RSS) model and its hybridization with the M5 Pruning tree (M5P), Random Forest (RF), and Random Tree (RT) to estimate the SPI at 3, 6, and 12 droughts during 2000–2019. Performances of RSS and hybridized algorithms were assessed and compared using performance indicators (i.e., MAE, RMSE, RAE, RRSE, and R2) and various graphical interpretations. Results indicated that the RSS-M5P provided the most accurate SPI prediction (MAE = 0.497, RMSE = 0.682, RAE = 81.88, RRSE = 87.22, and R2 = 0.507 for SPI-3; MAE = 0.452, RMSE = 0.717, RAE = 69.76, RRSE = 85.24, and R2 = 0.402 for SPI-6. And MAE = 0.294, RMSE = 0.377, RAE = 55.79, RRSE = 59.57, and R2 = 0.783 for SPI-12) compare to RSS alone, RSS-RF, and RSS-RT models for study the drought situation in Jaisalmer Rajasthan. The M5P algorithms have improved the performance of the RSS structure.
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Abbreviations
- SPI:
-
Standard Precipitation Index
- RSS:
-
Random Subspace
- M5P:
-
M5 Pruning tree
- RF:
-
Random Forest
- RT:
-
Random Tree
- MAE:
-
Mean absolute error
- RMSE:
-
Root mean square error
- RAE:
-
Relative absolute error
- RRSE:
-
Root relative squared error
- R2 :
-
Coefficient of determination
- P:
-
Precipitation
- PET:
-
Potential evapotranspiration
- °C:
-
Degree celsius
- mm:
-
Millimetre
- DrinC:
-
Drought Indices Calculator
- SDR:
-
Standard deviation reduction factor
- SE:
-
Standard error
- ML:
-
Machine learning
- MPMR:
-
Minimum probability machine regression
- ELM:
-
Extreme learning machine
- OSELM:
-
Online sequential-ELM
- ANNs:
-
Artificial neural networks
- SVR:
-
Support vector regression
- WA-ANN:
-
Coupled wavelet-anns
- MLP:
-
Multilayer perceptron
- MLP-ICA:
-
Imperialistic Competitive Algorithm-MLP
- MSPI:
-
Multivariate Standardized Precipitation Index
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AE, MK and NLK: Conceptualization, Methodology, Formal analysis, Software, Writing-Original draft preparation. NLK, DKV, MK, CBP, PD, and AS: Visualization, Comments and Revisions recommendations, Writing-Reviewing and Editing. AE, CBP, PD: Supervision, Comments and Revisions Recommendations, Writing-Reviewing and Editing.
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Elbeltagi, A., Kumar, M., Kushwaha, N.L. et al. Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India. Stoch Environ Res Risk Assess 37, 113–131 (2023). https://doi.org/10.1007/s00477-022-02277-0
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DOI: https://doi.org/10.1007/s00477-022-02277-0