Drought variability assessment using standardized precipitation index, reconnaissance drought index and precipitation deciles across Balochistan, Pakistan

Qaisrani, Zahid Naeem; Nuthammachot, Narissara; Techato, Kuaanan; Asadullah,; Jatoi, Ghulam Hussain; Mahmood, B.; Ahmed, Raees

doi:10.1590/1519-6984.261001

Cited by 13 publications

(6 citation statements)

References 60 publications

(83 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the regression based on the RF algorithm comprised an R 2 value of 0.67 and a RMSE of 23.93 boxes ha −1 (Figure 7b) using the same Vis. The results from RF modeling are also in agreement with the coefficients found in the literature for underground crops [43,44]. The results from the MLR model, after training, had a performance comprising an R 2 value of 0.61 and a RMSE of 24.21 boxes ha −1 (Figure 7c).…”

Section: Assesement Of Model Performancesupporting

confidence: 88%

AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture

de Lima Silva,

Furlani,

Canata

2024

AgriEngineering

View full text Add to dashboard Cite

The adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples were taken at 200 points with a 30 × 30 m sampling grid at 82 and 116 days after sowing in both areas. The total fresh biomass, aerial part, and root biometry were quantified for previous crop harvesting to measure yield. The quality of the roots was assessed by sub-sampling three carrots by the concentration of total soluble solids (°Brix) and firmness in the laboratory. Vegetation indices were extracted from satellite imagery. The most important variables for the predictive models were selected by principal component analysis and submitted to the Artificial Neural Network (ANN), Random Forest (RF), and Multiple Linear Regression (MLR) algorithms. SAVI and NDVI indices stood out as predictors of crop yield, and the results from the ANN (R2 = 0.68) were superior to the RF (R2 = 0.67) and MLR (R2 = 0.61) models. Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables.

show abstract

Section: Assesement Of Model Performancesupporting

confidence: 88%

AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture

de Lima Silva,

Furlani,

Canata

2024

AgriEngineering

View full text Add to dashboard Cite

show abstract

“…The Standardized Precipitation Evapotranspiration Index (SPEI) is a popular index for evaluating drought conditions. It has been used in various studies to analyze drought patterns and severity (Qaisrani et al, 2022) (Kartika & Wijayanti, 2023) (Öney & Anli, 2023) (Kumari et al, 2023).…”

Section: Methods For Calculating Drought Indexmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

View full text Add to dashboard Cite

Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, particularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought prediction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to conventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

show abstract

“…The Standardized Precipitation Evapotranspiration Index (SPEI) is a popular index for evaluating drought conditions. It has been used in various studies to analyze drought patterns and severity [30][31][32][33]. The SPEI combines meteorological and hydrological variables, such as precipitation, evapotranspiration, and groundwater levels, to provide a comprehensive assessment of drought [34].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duong,

Phong,

et al. 2024

Preprint

View full text Add to dashboard Cite

Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

show abstract

Drought variability assessment using standardized precipitation index, reconnaissance drought index and precipitation deciles across Balochistan, Pakistan

Cited by 13 publications

References 60 publications

AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture

AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Contact Info

Product

Resources

About