A Responsible Artificial Intelligence Framework for Groundwater Modeling

Artificial Intelligence for Environmental Monitoring and Sustainable Development

This paper explores the role of AI in environmental monitoring and sustainable development, with specific emphasis on the Nigerian context. Using systems theory as the conceptual framework, the study highlights the diverse applications of AI, including remote sensing, air and water quality assessment, waste management, biodiversity conservation, and disaster risk prediction. Artificial Intelligence (AI) has emerged as a transformative tool in addressing the pressing challenges of environmental monitoring and sustainable development. By leveraging advanced machine learning algorithms, computer vision, and data analytics, AI enables efficient collection, processing, and interpretation of large-scale environmental data. Applications span across climate modelling, air and water quality assessment, biodiversity conservation, precision agriculture, renewable energy optimisation, and disaster risk management. AI-powered systems enhance the accuracy of predictions, support real-time monitoring, and facilitate informed decision-making for policymakers and stakeholders. Despite its vast potential, challenges such as data privacy, high energy consumption, technological inequality, and ethical considerations remain critical concerns. Integrating AI into environmental management requires collaborative frameworks that balance technological innovation with ecological and social responsibility. Furthermore, the paper examines how AI contributes to the achievement of Sustainable Development Goals (SDGs), particularly Goals 6, 7, 11, and 13, fostering resilient ecosystems and promoting a greener, more sustainable future.

AI-driven groundwater mapping: systematic review and implications for practical uptake

Recent advances in artificial intelligence and machine learning have transformed groundwater mapping by enabling data-driven integration of heterogeneous geospatial, environmental, and hydrogeological information. This paper provides a critical review of AI-based groundwater mapping, synthesizing more than 200 peer-reviewed studies published between 2009 and 2026, with emphasis on the rapid methodological developments of the last 5 years. We outline the conceptual basis of AI-driven mapping approaches as the natural evolution from expert-based GIS overlays and statistical methods. Four dominant areas of application are identified: groundwater potential mapping, spatial prediction of groundwater quality and contamination, vulnerability assessment, and the delineation of groundwater-dependent ecosystems. Ensemble trees, gradient boosting algorithms, and neural networks are the most widely adopted methods, largely due to their ability to capture nonlinear relationships, handle multicollinearity, and perform well with heterogeneous datasets. We also explore the challenges involved in defining the minimum viable dataset size, as well as the increasing interpretability of machine learning outcomes. Despite improvements in predictive accuracy and spatial generalization, this review highlights persistent limitations that constrain real-world adoption. These include limited and uncertain subsurface data, overreliance on surface-derived proxy variables, restricted model transferability, insufficient treatment of uncertainty, and challenges related to model interpretability and transparency. We conclude by identifying priority research directions, including explainable AI, physics-informed and hybrid modeling strategies, spatio-temporal integration, and stronger links between AI outputs and groundwater management decisions.

Artificial intelligence and analytical techniques for groundwater quality assessment: a systematic review

AI- POWERED CLIMATE MODELLING: A LEGAL ANALYSIS OF DATA GOVERNANCE AND PRECAUTIONARY REGULATION

With the advancement of technology and growing climate crisis, artificial intelligence has emerged as a significant tool for Epredicting change in the climate and natural calamities with precision. AI models, today process and analyse large data sets to provide minute details regarding a slight rise in the sea level, extreme changes in weather and increased carbon emissions that traditional physics-based models fail to recognize. However, the integration of artificial intelligence into climate prediction introduces legal challenges that remain largely unaddressed by international as well as domestic frameworks. The core issue addressed in this paper is the responsibility gap created by the ‘black-box’ nature of AI- based climate predictions. When policy-oriented decisions such as urban zoning, investments in infrastructure and emergency evacuations are based on algorithm that later proves to be biased or inaccurate on the basis of data stored in the model, the problem of accountability arises. Furthermore, the paper examines the friction surrounding data governance and the importance of ‘right to information’ for public climate adaptation. The doctrinal analysis of emerging legislations such as the EU AI Act and the India’s Digital Personal Data Protection Act, 2023 will be done in order to evaluate how precautionary principle of environmental law can be implemented within the artificial intelligence framework. This paper proposes Sustainability by Design framework along with other suggestions. This framework advocates for mandatory transparency in training data, standardizing audit protocols for AI based climate model and a multifaceted liability framework to ensure that AI serves as a reliable instrument for climate justice.

Artificial Intelligence and Remote Sensing for optimizing Agricultural Water Use: A Systematic Mapping Study

Water scarcity and climate variability are placing increasing pressure on agricultural systems, necessitating innovative approaches to sustainable water management. This study presents a systematic review of the integration of artificial intelligence (AI) and remote sensing for optimizing water use in agriculture. A total of 2,817 publications were identified from major scientific databases, of which 67 peer-reviewed studies were selected for detailed qualitative and quantitative analysis. The results reveal a methodological shift toward integrated AI frameworks, with hybrid machine learning approaches being the most widely adopted, accounting for approximately 22% of the analyzed studies. This study provides a structured synthesis of current methodologies, identifies emerging trends, and highlights key research gaps. While AI and remote sensing show strong potential for improving water use efficiency and supporting climate-resilient agriculture, challenges remain, including data limitations, model transferability, and barriers to adoption. Future research should focus on scalable, explainable, and regionally adaptable AI solutions to facilitate large-scale deployment.

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

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.