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Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models

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Title Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models
Not Available
 
Creator Amuktamalyada Gorlapalli
Supriya Kallakuri
Pagadala Damodaram Sreekanth
Rahul Patil
Nirmala Bandumula
Gabrijel Ondrasek
Meena Admala
Channappa Gireesh
Madhyavenkatapura Siddaiah Anantha
Brajendra Parmar
Brahamdeo Kumar Yadav
Raman Meenakshi Sundaram
Santosha Rathod
 
Subject drought
water stress
standardized precipitation index
SPI3
SPI6
artificial intelligence
auto-regressive integrated moving average
artificial neural network
support vector regression
 
Description Not Available
In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration
and induces water stress in plants. The objective in this study was to characterize and forecast water
stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation
data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and
modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial
intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model.
The results show that on the short-term SPI3 time scale the studied region experienced extreme water
deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991,
2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6
time scales by AI models outperformed the classical ARIMA models in both, training and validation
data sets. Among applied models, the SVR model performed better than other models in modeling
and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano
test confirmed that SVR output was significantly superior. A reduction in the prediction error of
SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data
sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model
to account for the nonlinear and complex patterns in the input data sets against the classical linear
ARIMA model. These results may contribute to more sustainable and efficient management of water
resources/stress in cropping systems.
Not Available
 
Date 2023-10-21T09:20:07Z
2023-10-21T09:20:07Z
2022-05-30
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/80776
 
Language English
 
Relation Not Available;
 
Publisher Not Available