Browsing by Author "Kumar, Manish"

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    Role of urban green space structure and configuration in regulating land surface temperature in NCT Delhi using explainable artificial intelligence
    (2026) Kumar, Manish
    Urbanization-driven heat intensification poses a serious challenge to environmental sustainability and thermal comfort in megacities such as National Capital Territory (NCT) Delhi. The influence of green space structure and configuration on Land Surface Temperature (LST) remains under explored. This study presents a novel framework that integrates Fragstats-derived green space metrics and explainable artificial intelligence (XAI) to assess the spatio-temporal impact of green space structure and configuration on LST in NCT Delhi. The LST and nine green space metrics were derived using the Landsat-8 (OLI/TIRS) imagery. Three machine and deep learning models i. e., Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL) were developed to regress and predict LST using H2O AutoML package in Rstudio environment. Among these, GBM produced the highest predictive accuracy (R 2 = 0.8859) and was therefore selected for further interpretation using XAI techniques such as SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots. Results show that fragmented green spaces intensify surface heating, while cohesive and well-connected green space corridors pro mote cooling through enhanced evapotranspiration and shading. The findings highlight that not only the amount but also the spatial configuration of vegetation determines its cooling efficiency. Areas in East Delhi (Shahdara, Seelampur) and North-West Delhi (Narela, Bawana, Rohini Extension), characterized by high fragmentation, experience higher temperatures, whereas contiguous green space corridors of Central and South Delhi exhibit stronger cooling benefits. These insights provide actionable guidance for urban planners and policymakers to prioritize cohesive green space development in future urban planning for climate adaptation and mitigation.
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    Unraveling intricacies of monsoon attributesin homogenous monsoon regions of india
    (Frontiers in Earth Science, 2022) Saini, Atul; Sahu, Netrananda; Dhan, Weili; Kumar, Manish; Ramavtar; Mishra, Manoranjan; Kumar, Pankaj; Pandey, Rajiv; Behera, Swadhin
    India observes the summer monsoon in June–July–August–September (JJAS) season, and the livelihood security of a huge population depends on it. The impact of the monsoon onset timing, length of monsoon season, rainfall amount, and related extreme events is huge on the Indian economy. Therefore, understanding the inherent intricacies needed a detailed investigation. In five homogenous monsoon regions of India, the trend of monsoon onset and the length of monsoon season are examined. The association between 1) monsoon onset ~ rainfall amount, 2) length of monsoon season ~ rainfall amount, and 3) monsoon onset ~ length of monsoon season is investigated. Subsequently, the behavior of rainfall and extreme excess days in the ±1 standard deviation (SD) length of monsoon season is also examined in detail. The trend for monsoon onset shows late onset in all the homogenous monsoon regions except the northeast region. The length of monsoon season is found increasing significantly with high magnitude in west central and northwest regions. A significantly strong negative correlation (~−0.6) for monsoon onset timing ~ length of monsoon season is observed. Therefore, the change in rainfall anomaly, extreme excess days, and rainy days is done concerning the length of the monsoon season. In the cases of the −1 SD (+1 SD) length of monsoon season, rainfall anomaly and extreme excess days are low (high) in most parts of the homogenous monsoon regions. Extreme excess days showed a significant association with rainy days, which indicates a high possibility of rainy days converting into extreme excess days. However, the increase in extreme excess days in the +1 SD length of monsoon season is limited to a great extent in JJAS and June only. Morlet wavelet power spectrum shows the delay (advance) of power in −1SD (+1 SD) length of monsoon season.
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