Intellectual Property Challenges of AI-Based Groundwater Salinity Forecasting in the Indian Sundarbans

Abstract

Artificial intelligence (AI)–driven environmental forecasting is rapidly transforming climate-risk assessment in vulnerable deltaic ecosystems. However, the intellectual property (IP) dimensions of such predictive systems remain underexplored. This study examines the intellectual property implications of an AI-governed groundwater salinity forecasting framework developed for the Indian Sundarbans, one of the world’s most climate-sensitive mangrove deltas. Using a multi-decadal dataset (1990–2020) from ten monitoring stations across contrasting estuarine regimes, a Neural Autoregressive (NAR) model was implemented to generate seasonal salinity projections up to 2050. The framework integrates algorithmic forecasting, normalized salinity risk indexing (0–1 scale), sectoral heatmap visualization, and climate-risk classification tools for aquifer vulnerability assessment.

Beyond hydrological insights, this research critically evaluates the protectable components of environmental AI systems, including algorithm architecture, predictive workflows, database rights, risk-classification methodologies, and decision-support interfaces. The paper explores whether such integrated forecasting systems qualify for copyright protection, software IP registration, database rights, or patentability under emerging digital-environmental governance frameworks. It also interrogates issues of data sovereignty, algorithmic transparency, public-resource governance, and ethical constraints when predictive tools influence drinking-water planning and climate adaptation strategies.

The findings suggest that while core machine-learning models may not be independently patentable, novel system integration, customized environmental risk indices, and structured decision-support outputs may constitute protectable intellectual assets. The study highlights the tension between proprietary environmental intelligence and public-interest access in climate-vulnerable regions of the Global South. By situating AI-based groundwater forecasting within the broader discourse of intellectual property rights and digital water governance, this paper contributes to the emerging field of environmental algorithm jurisprudence and calls for balanced regulatory frameworks that protect innovation without compromising climate justice and community resilience.