Research Areas

UNESCO's Man and Biosphere Program, initiated in 1971, addresses the intricate challenges of today by intertwining natural environments with local communities, animal life, and marine biodiversity. Biosphere Reserves, curated by UNESCO, represent a balance between conservation, economic development, and cultural preservation. These spaces, recognized within the World Network of Biosphere Reserves, aim for sustainable development and global problem-solving through local solutions. Despite India having 18 registered sites in the network, there has been a lack of new biosphere reserves in the last five years. The project "Discerning the Importance of Biosphere Reserves: The Sindhudurg Case" focuses on prescribing the reasons and methods for establishing a biosphere reserve in Sindhudurg, Maharashtra. Through secondary sources and qualitative research, this work aims to provide recommendations based on themes, actors, accountability, and permanent features, intending to go beyond Sindhudurg to contribute to economic, social, political, and environmental development.

The APY analysis tool addresses the surging demand for seafood and marine products by promoting sustainable practices in aquaculture. Recognizing the need for correct farming guidelines, the research focuses on inland aquaculture's environmental and economic sustainability. Emphasizing the impact on water contamination and adjacent biomes, the tool aims to maximize production per contamination footprint. It seeks to achieve better governance by providing crucial aquaculture data for regulatory monitoring, facilitate knowledge sharing among farmers for improved livelihoods, and enhance returns on aquaculture investments through predictive insights and resource optimization. The tool's objectives include conducting Area, Production, and Yield analysis, building a Realtime Aquaculture farm tracking system, integrating IoT with Machine learning, developing a smartphone application for accessibility, and contributing to digital transformation for sustainable aquaculture practices and increased economic returns.

The APY analysis tool, developed as an Android application, represents a groundbreaking approach to shrimp and seaweed farming amidst climate change challenges. Leveraging the UDA framework and digital transformation, the tool employs a detailed source-path-receiver model for qualitative and quantitative understanding of

ground realities, facilitating real-time monitoring of underwater parameters. Validation through sensor networks and platform deployment ensures accurate data collection, pre-processed to eliminate errors and tropical distortions. The tool's key functionalities include available data analysis and parameter-based analysis, providing farmers with comprehensive insights and recommendations. Developed in Python with the Django web framework, the tool integrates seamlessly with the Android application, enhancing accessibility and user convenience. The application, built using Android Studio and utilizing AWS for data storage, allows farmers to access real-time data, make informed decisions, and optimize farming practices on the go. The APY analysis tool revolutionizes aquaculture analysis, empowering farmers to enhance production, allocate resources efficiently, and promote sustainable practices, ultimately driving growth in the industry

Biofouling, the attachment and growth of organisms on submerged surfaces like ship hulls, poses significant challenges in the maritime sector, leading to increased fuel costs, maintenance needs, and operational disruptions. Ship hulls, comprising 24% of fouled items, are a focal point for this issue. Research explores eco-friendly alternatives to traditional, environmentally harmful antifouling coatings. While antifouling paints discharge toxic substances, ultrasonic anti-fouling technologies offer a viable substitute by using waves to inhibit marine adhesion. This study delves into the fouling mechanisms of global anti-fouling solutions, emphasizing the need for future developments to manage fouling in tropical waters. Specific objectives include identifying the causes of accelerated fouling, reviewing existing anti-fouling methods, considering international regulatory provisions, and assessing the feasibility of implementing customized solutions for naval platforms, accounting for hardware, software, and local biological factors through literature reviews, lab-based testing, and field experiments led by Prof Varsha.

The creation of a continuous Passive Acoustic Monitoring (PAM) network underwater holds various applications for both military and civilian purposes, contributing to Enhanced Underwater Domain Awareness (UDA) for effective maritime governance. Real-time monitoring of low-frequency ambient noise offers valuable insights into underwater propagation, vessel movements, marine mammal activity, and acoustic parameters. This innovative initiative aims to deploy passive acoustic sensors in the tropical littoral waters of the Indian Ocean Region (IOR) for the first time, focusing on monitoring low-frequency ambient noise characteristics. Achieving effective UDA in these waters promotes safe, secure, and sustainable growth, making this research a unique and novel undertaking that enhances the country's acoustic capabilities for improved maritime governance.

The distinct tropical conditions within the Indian Ocean Region (IOR) present distinctive challenges and possibilities in the realm of freshwater management, encompassing both water resource and water quality aspects. Managing excessive siltation necessitates substantial efforts in sediment management to maintain adequate storage capacity, while ensuring the quality of available water meets hygiene standards. Effective water quality monitoring demands accurate prediction models and field validation. Recognizing the unique interplay of water resource and quality, an integrated approach becomes crucial, considering the provision of high-quality freshwater with varying quality requirements. A calibrated strategy is imperative for real-time water quality monitoring, ensuring each user receives freshwater allocation based on their specific application needs. Given the dynamic nature of water sources, continuous monitoring of water quality is essential for timely interventions tailored to specific application requirements.

In this study, we are developing a framework to generate a dynamic three-dimensional spatio-temporal map of low-frequency ambient noise for a specific region in the Indian Ocean, encompassing the Indian Subcontinent (0.125° S - 60.125° E to 25.125° N – 90° E). The map is a quarter-degree gridded representation of ambient noise levels, with receiver points being spaced at intervals of 0.25°, capturing and mapping noise levels at each point. We are utilizing Automatic Identification System (AIS) data for our area of interest to extract shipping traffic information. The source spectra of ships are being calculated using a modified version of D. Ross's formula, incorporating ship length and speed as input parameters. Additionally, a Neural Network model trained for the Indian Ocean Region is being employed to compute the transmission loss between each pair of sender (ship) and receiver points.

This ongoing research focuses on the sedimentation challenges prevalent in one of India's largest rivers, the Brahmaputra River. It sheds light on the various issues affecting the river and underscores how gaps in current policies hinder prompt issue resolution. These concerns, occasionally highlighted by eminent authors well-versed in the river's dynamics, prompt a critical examination. Additionally, the study explores management measures implemented by China to address sedimentation issues in the Yellow River. By incorporating a global perspective, particularly from the Yellow River context, the research

aims to glean insights applicable to resolving Brahmaputra-related challenges. It advocates for the establishment of an effective and integrated river management system or institution solely dedicated to overseeing the Brahmaputra River.

This ongoing research explores the application of machine learning in modeling underwater channels, considering the prevalence of acoustic waves in the strategic Indian Ocean Region (IOR). The region's tropical littoral waters present challenges for effective sonar deployment due to a high SOFAR channel depth, resulting in acoustically shallow conditions with multiple interactions of acoustic propagations with surface and bottom boundaries. The complexities are further heightened by random fluctuations in surface roughness and bottom type. The research addresses the need to map underwater parameters such as wind, speed, surface temperature, salinity, sound speed profile, bottom type, and profile. Employing Modeling & Simulation (M&S) to map the underwater channel across all seasonal and diurnal variations is crucial for containing the impact on acoustic propagation. Real-time prediction of underwater channel behavior through this M&S effort aims to mitigate its impact. Current mathematical methods for acoustical channel modeling, based on differential equations and various assumptions, are limited to specific regions. Given the variability of IOR waters, we propose a data-centric approach utilizing state-of-the-art machine learning algorithms to learn from simulation data, offering a more adaptable solution.

In this study, we address the physiological risks associated with underwater diving, considering the potential impact of rising levels of underwater noise, especially in the Indian Ocean Region (IOR). While diving is a common exercise, physiological risks stemming from natural water characteristics and inadequate training can compromise diver efficiency. The degradation of marine ecosystems due to increased anthropogenic activities in coastal areas has further heightened health hazards. The acoustic ecology of the underwater habitat, often overlooked, poses biohazards from underwater degradation. Divers exposed to elevated underwater noise levels may experience physiological effects, ranging from annoyance and temporary disorientation to potential hearing loss and mechanical injuries to the ear. Existing legislations provide guidelines on occupational noise levels, but there are no defined standard thresholds for underwater use. Previous research indicates variations in sound level reference values, complicating evidence review. Additionally, the lack of publicly available human audiograms in water poses a challenge. Addressing these gaps, our study aims to develop a hazard analysis tool for assessing the probability of physiological damage to divers based on the acoustic

ecology of the region. The tool, incorporating a color-coded ambient 3D noise map, will identify safe diving zones. By utilizing real-time inputs such as environmental parameters, approximate diving depths, AIS data, wind speed, and independent noise sources like offshore drilling, the tool will automatically estimate total ambient noise in the region, accounting for transmission losses due to ocean water conditions. The cumulative noise is calculated using a source-path-receiver model.