An explainable machine learning technique to forecast lightning density over North-Eastern India

Mandal, Joyjit; Chatterjee, Chandrani; Das, Saurabh

doi:10.1016/j.jastp.2024.106255

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2024

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Cited by 2 publications

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Interpretable Artificial Intelligence Models for Predicting Lightning Prone to Inducing Forest Fires

Song,

Zhou,

Yuan

et al. 2024

Preprint

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Interpretable Artificial Intelligence Models for Predicting Lightning Prone to Inducing Forest Fires

Song,

Zhou,

Yuan

et al. 2024

Preprint

View full text Add to dashboard Cite

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

Başakın,

Stoy,

Demirel

et al. 2024

Remote Sensing

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We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

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An explainable machine learning technique to forecast lightning density over North-Eastern India

Cited by 2 publications

References 32 publications

Interpretable Artificial Intelligence Models for Predicting Lightning Prone to Inducing Forest Fires

Interpretable Artificial Intelligence Models for Predicting Lightning Prone to Inducing Forest Fires

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

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