Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree

Chaitanya B. Pande, Nadhir Al-Ansari (), N. L. Kushwaha, Aman Srivastava, Rabeea Noor, Manish Kumar, Kanak N. Moharir and Ahmed Elbeltagi ()
Additional contact information
Chaitanya B. Pande: Indian Institute of Tropical Meteorology, Pune 411008, India
Nadhir Al-Ansari: Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
N. L. Kushwaha: Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India
Aman Srivastava: Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur 721302, India
Rabeea Noor: Department of Agricultural Engineering, Bahuddin Zakariya University, Multan 34200, Pakistan
Manish Kumar: College of Agricultural Engineering and Technology, Dr. R.P.C.A.U., Pusa 848125, India
Kanak N. Moharir: Indian Institute of Forest Management, Bhopal 462003, India
Ahmed Elbeltagi: Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt

Land, 2022, vol. 11, issue 11, 1-24

Abstract: Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R 2 and Adjusted R 2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations.

Keywords: standard precipitation index; drought forecasting; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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