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

Pande, Chaitanya B. (författare)
Indian Institute of Tropical Meteorology, Pune 411008, India
Al-Ansari, Nadhir, 1947- (författare)
Luleå tekniska universitet,Geoteknologi
Kushwaha, N. L. (författare)
Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur 721302, India
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Srivastava, Aman (författare)
Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur 721302, India
Noor, Rabeea (författare)
Department of Agricultural Engineering, Bahuddin Zakariya University, Multan 34200, Pakistan
Kumar, Manish (författare)
College of Agricultural Engineering and Technology, Dr. R.P.C.A.U., Pusa 848125, India
Moharir, Kanak N. (författare)
Indian Institute of Forest Management, Bhopal 462003, India
Elbeltagi, Ahmed (författare)
Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
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 (creator_code:org_t)
2022-11-14
2022
Engelska.
Ingår i: Land. - : MDPI. - 2073-445X. ; 11:11
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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 R2 and Adjusted R2 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Water Engineering (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

standard precipitation index
drought forecasting
machine learning
Soil Mechanics
Geoteknik

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