Omar et al. (2026) Harnessing Machine Learning for LULC Dynamics and Hydrological Predictions in Water Resources

Identification

• Journal: Lecture notes in civil engineering
• Year: 2026
• Date: 2026-01-01
• Authors: Padam Jee Omar, Manvendra Singh Chauhan, Ashish Kumar Kashyap
• DOI: 10.1007/978-981-95-0736-8_8

Research Groups

• Department of Civil Engineering, UIET, Babasaheb Bhimrao Ambedkar University, Lucknow, India
• Department of Civil Engineering, Amity University, Noida, Uttar Pradesh, India

Short Summary

This study investigates the application of machine learning (ML) techniques to analyze land use and land cover (LULC) changes and their hydrological impacts in urban Lucknow, India, demonstrating that ML models significantly enhance the accuracy and reliability of hydrological predictions compared to traditional models.

Objective

• To investigate the role of machine learning (ML) in analyzing land use and land cover (LULC) changes and their impacts on hydrology in the urban context of Lucknow, India.
• To assess the effectiveness of various ML models (regression models, decision trees, and neural networks) in predicting key hydrological variables such as precipitation, runoff, and evapotranspiration.

Study Configuration

• Spatial Scale: Urban context of Lucknow, India.
• Temporal Scale: Not explicitly defined for the study period, but focuses on analyzing past LULC changes and predicting future hydrological impacts due to rapid urbanization.

Methodology and Data

• Models used: Regression models, decision trees, neural networks.
• Data sources: Hydrological data (precipitation, runoff, evapotranspiration), Land Use and Land Cover (LULC) data (implied from the analysis of LULC changes, likely derived from remote sensing and ground observations).

Main Results

• Machine learning models significantly outperform traditional hydrological models in terms of accuracy and reliability for predicting key hydrological variables.
• Rapid urbanization in Lucknow has led to significant LULC alterations, resulting in increased impervious surfaces, higher surface runoff, and reduced groundwater recharge, exacerbating flooding and water scarcity.
• ML models provide robust tools for predicting these changes and enabling proactive management strategies for urban water resources.

Contributions

• Demonstrates the transformative potential of machine learning in urban water resource management by offering more precise and actionable insights than traditional hydrological approaches.
• Highlights the capacity of ML models to effectively handle large datasets and identify complex patterns in LULC dynamics and their hydrological consequences.
• Proposes future research directions, including the integration of real-time data for dynamic modeling and the development of hybrid models combining ML with traditional hydrological methods.

Funding

• Seed money provided by BBA University, Lucknow, U.P., India (letter no. IQA11606-L/BBAU/2024, dated 08 April 2024).

Citation

@article{Omar2026Harnessing,
  author = {Omar, Padam Jee and Chauhan, Manvendra Singh and Kashyap, Ashish Kumar},
  title = {Harnessing Machine Learning for LULC Dynamics and Hydrological Predictions in Water Resources},
  journal = {Lecture notes in civil engineering},
  year = {2026},
  doi = {10.1007/978-981-95-0736-8_8},
  url = {https://doi.org/10.1007/978-981-95-0736-8_8}
}