About
Danish Khan is a dedicated researcher with a keen interest in the intersection of geospatial technology and sustainable development. Their academic pursuits focus on urban growth modeling, land use and land cover analysis, and the application of machine learning and explainable artificial intelligence (XAI) in environmental studies. With expertise in satellite imagery analysis, GIS-based modeling, and spatial data analytics, they have contributed significantly to understanding urbanization dynamics and their environmental implications. Their current research explores predictive urban growth patterns using advanced machine learning techniques, emphasizing sustainable urban planning and governance.
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Publications (13)
- Satellite-derived climatology of paddy-residue fires and post-monsoon aerosol loading in Punjab’s Malwa region (2000–2024) Save
- Multi-decadal geospatial trend analysis reveals anthropogenic control of groundwater-level decline across the Malwa region of Northwestern India Save
- Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method Save
- Ensemble machine learning for predicting the urban expansion in Lucknow, India Save
- Harnessing hybrid intelligence and explainable AI for urban growth prediction: A Data-Driven framework for sustainable cities Save
- Modelling urban future: integrating CA-ANN model for comprehensive understanding of land use, land cover changes, and temperature dynamics in Lucknow City, India Save
- Hybrid Intelligence and Explainable AI for Urban Growth Prediction Modelling Save
- Decoding urban expansion: a machine learning perspective on Lucknow's growth trajectory Save
- Urban Expansion and Spatial Growth Patterns in Lucknow: Implications for Sustainable Development (1991–2021) Save
- Urban Expansion and its Influence on Land Surface Temperature: A Case Study of Patna City, India Save