Optimising water consumption in waterlogged lands using genetic algorithm in order to improve environmental conditions
DOI:
https://doi.org/10.24425/jwld.2025.156055Abstract
Waterlogging significantly undermines agricultural productivity and environmental sustainability, particularly in regions like South Asia where conventional, static management strategies fail to address complex site-specific dynamics. To overcome these limitations, this study introduces a novel genetic algorithm-based optimisation framework designed to balance the conflicting objectives of water removal, crop yield, and environmental health. The methodology involved coupling the DRAINMOD hydrological model with an evolutionary algorithm across 50 diverse agro-ecological sites in the Indo-Gangetic Plain. By integrating granular data on soil moisture, crop requirements, and drainage parameters, the framework evolved optimal management strategies, including specific adjustments to drain depth, spacing, and land levelling. The application of this model yielded substantial improvements over baseline practices, demonstrating a 37.2% reduction in waterlogging duration and a 21.9% increase in crop yields. Furthermore, the optimised strategies enhanced water use efficiency by 35.4% and reversed soil organic carbon depletion, effectively transforming waterlogged soils from a carbon source to a sink. Economic analysis indicated a 29.8% increase in net present value, while scenario analyses confirmed the system’s resilience to projected 2050 climate conditions. These findings suggest that adopting data-driven, adaptive optimisation tools offers a viable pathway for sustainable intensification. Practical implementation requires institutional support for infrastructure upgrades, such as deeper drainage systems and precision levelling, to realise these socioeconomic and ecological benefits.References
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