Geographically Weighted Regression Hybridized with Kriging Model for Delineation of Drought-Prone Areas

Bahmani, Shadi; Naganna, Sujay Raghavendra; Ghorbani, Mohammad Ali; Shahabi, Mahmoud; Asadi, Ebrahim; Shahid, Shamsuddin

doi:10.1007/s10666-021-09789-z

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…Drought indices include the standardized precipitation index (SPI) [10], the Palmer Drought Severity Index (PDSI) [11], the Precipitation anomaly (Pa) [12], and the Standardized Precipitation Evapotranspiration Index (SPEI) [13]. SPI, based on station precipitation, is favoured by many because of the ease of access to the required rainfall information and the simplicity of the calculation [14,15], and has been widely used in research for identifying drought events in different regions [16], evaluating drought indicators [17], analysing the intensity of drought [18], analysing the frequency of drought [19], identifying drought-prone areas [20], as well as monitoring and evaluating drought [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Variation Characteristics and Driving Factors of Drought in Yinshanbeilu Inner Mongolia Based on a Cloud Model

Zhang,

Fu,

Wang

et al. 2024

Water

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Drought refers to a meteorological disaster that results in insufficient soil moisture due to a long-term lack of rainfall and disrupts the moisture balance of crops. Yinshanbeilu in Inner Mongolia is an arid and semi-arid region, and the onset of drought and its subsequent transmission is a key challenge in water resource management. This study takes Yinshanbeilu as the study area, analyses the changing characteristics of multi-timescale drought from 1971 to 2020 based on the Standardised Precipitation Index (SPI), and analyses the stochasticity and stability of the drought by using a cloud model. Finally, the cross-wavelet transform method and Pearson’s test are used to explore the correlation between atmospheric circulation factors, PRE and PET, and drought. The results indicate that (1) on the annual scale, the frequency of drought in Yinshanbeilu mainly ranges from 22% to 28%, with a high frequency of light droughts, a low frequency of severe droughts, a high frequency of droughts in the east and west, and a low frequency of droughts in the north and south; on the seasonal scale, the frequency of droughts in winter is the highest, with a rate of 34.6%, and the lowest frequency of droughts is in autumn, with a rate of 28.3%. (2) There is a decreasing trend in Entropy (En) and Hyper-Entropy (He), and an increasing trend in Expectation (Ex) for the inter-annual SPI-12 cloud model. Spatially, Ex and He are negatively correlated, while En and He are positively correlated. The inter-annual variation in cloud eigenvalues is greater than the inter-site variation, so the cloud model better reflects the spatial stochasticity and stability of regional inter-annual SPI. For the seasonal-scale SPI-3 cloud model, Ex is smaller in all seasons, En is also smaller, and He is larger. (3) Sunspot, PRE (precipitation), and PET (Potential Evapotranspiration) are all positively correlated with SPI and have the highest correlation. This study reveals the characteristics and causes of variations of drought in Yinshanbeilu, which can be applied to future research areas related to regional drought risk management.

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Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Variation Characteristics and Driving Factors of Drought in Yinshanbeilu Inner Mongolia Based on a Cloud Model

Zhang,

Fu,

Wang

et al. 2024

Water

View full text Add to dashboard Cite

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“…This method provides the best unbiased estimates and can be classified into stochastic estimation where statistical calculations are performed to produce interpolations [17]. This method has an estimator with a minimum variance and is a linear combination of observations [18]. Moreover, this method is an interpolation technique that uses measured sample site values to estimate values at unmeasured sites using a semi variogram to obtain optimal weighting values [19].…”

Section: Introductionmentioning

confidence: 99%

“…The selection of semi variogram is not based on anything, all semi variogram modeling can be used with trial-and-error properties, but the commonly used semi variogram modelings are Spherical, Exponential, and Gaussian. The models formed will become Geographically Weighted Regression Kriging (GWRK) [18]. The GWRK model is a model formed by adding the GWR model predictions with the interpolation results of the GWR model errors.…”

Section: Introductionmentioning

confidence: 99%

Classification of Stunting Using Geographically Weighted Regression-Kriging Case Study: Stunting in East Java

Iriany

Ngabu

Arianto

et al. 2023

BAREKENG: J. Math. & App.

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Geographically Weighted Regression Kriging (GWRK) is a special case of Geographically Weighted Regression (GWR) model, which is modeling with the effect of spatial autocorrelation on the GWR model error. The purpose of this research is to obtain a GWRK model between the factors that affect stunting density for each site viewed from the district center point in East Java Province and to make a prediction map based on the GWRK modeling. The data used was obtained from Basic Health Research (RISKESDAS) and the East Java Health Profile Book for 2021. The units of observation in this study were 38 districts in East Java.. Based on the GWR modeling results, it was found that the GWR model error contained spatial autocorrelation so that GWR model can be formed. From the GWRK modeling using stunting prevalence data in East Java in 2021, it was found that the GWR model was better than the global regression. Through prediction and prediction mapping formed from the GWR-Kriging modeling, it could be seen that stunting in regencies in East Java was evenly distributed . The interpolation map showed that the stunting forecasting values using the Kriging GWR interpolation ranged from 27% to 46%.

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“…This is due to the fact that a drought's timing can be just as crucial as its duration. For instance, a drought that strikes during a critical time of year, such as the growing season, can be more severe than a drought that occurs during a less critical time of year [11]. The different drought time scales can also have a different impact on different environments.…”

Section: Introductionmentioning

confidence: 99%

Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches

Hukkeri,

Naganna,

Pruthviraja

et al. 2023

IEEE Access

Self Cite

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Depending on the severity and spatial-temporal variability, droughts can have a wide range of impacts such as crop failure, water shortages, and food insecurity. Accurate and timely forecasting is necessary to mitigate the hazards of extreme weather events, such as droughts, brought on by climate change. A district like Chitradurga in India, which typically receives around 450-600mm of annual rainfall, will require advanced drought mitigation strategies and plans before the onset of the drought. This research focuses on 1-step lead time forecasting of meteorological drought episodes making use of the 6-month Standardised Precipitation Index (SPI-6) as indicator. The fine resolution rainfall data (0.25°× 0.25°) obtained from the Indian Meteorological Department was used to derive the 6-month SPI data of 23 grid stations. The 1-step lead time SPI-6 time series was forecast considering the antecedent SPI-6 time series data as model input. The Mutual Information was used to determine the most relevant input features for drought forecasting. The standard artificial neural network, an advanced machine learning framework -multivariate adaptive regression splines, and the ensemble learning-based CatBoost regression and gradient tree boosting paradigms were employed to forecast drought episodes. Error and efficiency metrics were employed for performance evaluation of the simulated models. The multivariate adaptive regression splines and gradient tree boosting forecasts had slightly higher accuracy and lower error rates than the artificial neural network model, which suggests that they may be more reliable for drought forecasting. The root mean square error and normalized Nash-Sutcliffe efficiency ranges of the multivariate adaptive regression splines model (during test phase) were 0.37-0.54 and 0.78-0.87, respectively. The thematic maps that were created using spatial interpolation of model forecasts from all the stations also confirmed that the district as a whole experienced drought in April 2019.

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Geographically Weighted Regression Hybridized with Kriging Model for Delineation of Drought-Prone Areas

Cited by 8 publications

References 42 publications

Analysis of Spatiotemporal Variation Characteristics and Driving Factors of Drought in Yinshanbeilu Inner Mongolia Based on a Cloud Model

Analysis of Spatiotemporal Variation Characteristics and Driving Factors of Drought in Yinshanbeilu Inner Mongolia Based on a Cloud Model

Classification of Stunting Using Geographically Weighted Regression-Kriging Case Study: Stunting in East Java

Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches

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