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2024 | Book

Artificial Intelligence and Edge Computing for Sustainable Ocean Health

Editors: Debashis De, Diganta Sengupta, Tien Anh Tran

Book Series : The Springer Series in Applied Machine Learning

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Table of Contents

About this book

Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health. The growing influence of Artificial Intelligence (AI), along with the Internet of Things (IoT) in generating wide coverage of sensor networks, and Edge Computing (EC) has paved the way for investigation of underwater as well as massive marine data, thereby generating huge potential for credible research opportunities for these domains. This book’s journey begins with a broad overview of Artificial Intelligence for Sustainable Ocean Health, setting the foundation for understanding AI's potential in marine conservation. The subsequent chapter, Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism, illustrates the synergy between technological advancements and sustainable tourism practices, demonstrating how AI can enhance the attractiveness and preservation of marine destinations. The identification, restoration, and monitoring of marine resources along with the utilization of technology continues in Utilization of Underwater Wireless Sensor Network through Supervising a Random Network Environment in the Ocean Environment has been extensively dealt with. The technical challenges of underwater imaging, essential for accurate data collection and analysis has been discussed.

The importance of Explainable AI is discussed in chapters like Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health, Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health, and A Comprehensive Study of AI (XAI) for Ocean Health Monitoring, which emphasize transparency and trust in AI systems. Further, Revolutionizing Internet of Underwater Things with Federated Learning, Underwater Drone, Underwater Imagery with AI/ML and IoT in ROV Technology and Ocean Cleanup has been demonstrated using innovative approaches to addressing underwater challenges. The book also includes a Review on the Optics and Photonics in Environmental Sustainability, focusing on the role of optics in marine conservation. Security issues are tackled in Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission, and the overarching potential of AI in marine resource management is discussed in Artificial Intelligence as Key-enabler for Safeguarding the Marine Resources.

Frontmatter

AI for Ocean Health

Frontmatter

Artificial Intelligence for Sustainable Ocean Health

Abstract

Artificial intelligence has been increasingly applied in the field of ocean health to better understand and monitor the world’s oceans health. The ocean is a complex and dynamic system that is affected by different factors, including climate change, ocean warming, pollution, and overfishing. AI models have the capability to analyze substantial volumes of data to identify patterns and trends, but without explanations for their predictions and decisions, it can be difficult to interpret their results. This chapter explores the importance of explainable AI for ocean health, including the need for transparency, accountability, and trust in AI models. This chapter also discusses the current state of the art in explainable AI (XAI) for ocean health, including various techniques and methodologies that have been developed to provide explanations for AI models’ predictions and decisions. Ultimately, the objective of explainable AI for ocean health is to promote sustainable use of ocean resources, protect marine biodiversity, and alleviate the impacts of climate change on the oceans.

Mahamuda Sultana, Suman Bhattacharya, Nilanjana Adhikari, Diganta Sengupta, Debashis De

Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism

Abstract

This chapter examines how artificial intelligence (AI) alongside other cutting-edge technologies can protect the marine environment while simultaneously fostering responsible tourism. Oceans face immense challenges because they constitute crucial parts of our planet’s ecology and because of pollution, climate change, and overfishing. The tourism sector, which is attracted to the marine wonders, must also change to prevent further degradation of these sensitive areas by its processes. In this setting, advanced technologies and artificial intelligence (AI)-driven strategies represent potent instruments that promise to maintain the ocean’s biodiversity and beauty while continuing to enthrall and educate tourists. This chapter explores practical applications, moral considerations, and the possibility of a peaceful future while navigating the complex link between technological advances, protecting the oceans, and sustainable tourism. This chapter tries to give a thorough overview of the way AI and modern technology are influencing the conservation of the oceans and sustainable tourism in the future. An in-depth discussion is given to the difficulties, chances, and moral issues that emerge at this vital and dynamic nexus between human endeavors and the oceanic realm. The contributor demonstrated the opportunity for technologies to be an impetus in promoting ocean health and responsible tourist practices using an amalgam of scientific investigation and real-world experiences.

Birendra Kishore Roy

Leveraging the Power of AI for Sustainable Oceans

Abstract

Artificial intelligence (AI) is the leading-edge technology for making intelligent machines. Different sectors are leveraging the benefits of AI. Plastic pollution is a major environmental threat that has severely affected oceans. Natural habitats of marine animals were demolished. Millions of species succumbed to death after consuming plastic or getting tangled on it. Plastic waste materials are the possible carriers of pathogens that increase waterborne diseases such as cholera and malaria. When plastic enters the ocean, there is no limit—waves can transmit plastic to the farthermost extreme of the ocean. Plastic then accumulates in marine ecosystem. After several years, it becomes impossible to retrieve plastic from water bodies when they turn into smaller pieces. Plastic is nonbiodegradable. Urbanization has increased the utilization of plastic in our daily lives including furniture, grocery, and vehicular equipment. Plastic that is thrown to landfills pollutes the soil with deleterious chemicals. More than 70% of Earth is covered with water. Location of specific species can be identified through AI and based on the ecosystem, appropriate solutions could be executed to protect them. Overfishing can be brought under control by analyzing the patterns of fish migration. AI drones are designed in such a way that they identify plastic debris in oceans and assist scientists in cleaning up process. Machine learning (ML) models for predicting the distribution of marine lives based on environmental requirement can be built. AI can work on efficiently identifying plastics inside the oceans, thus bringing down plastic pollution. Open water and color of coastal are few parameters that can be considered for determining ocean health through ML techniques. Machine learning has become the foundation of many AI novel solutions. In this chapter, we will go through introduction to AI and ML understanding the problem of plastic waste for marine lives, background work done by researchers so far, application of AI to monitor ocean health, ocean pollution degrading human well-being, case studies of AI for ocean conservation, ML practical implementation, shortcoming of AI adoptability, conclusion, and future scope.

Medini Gupta, Sarvesh Tanwar

Blue Ocean and Machine Learning Trajectories SDG 14: Life Below Water for Handling Ocean Pollution: Metaverse Conserve Ocean Health Sustainability Through the Lens of Transboundary Legal-Policy Regulations as Articulating Space for Futuristic Changes

Abstract

Blue Ocean Health refers to the concept of maintaining the health and sustainability of Earth’s oceans. It encompasses a broad range of efforts aimed at preserving the ecological balance, biodiversity, and overall well-being of marine ecosystems. Blue Ocean Health is a critical component of Sustainable Development Goal 14 (SDG 14)—Life below Water. Sustainable Development Goal 14 (SDG 14) calls for concerted global efforts to safeguard the world’s oceans, seas, and marine ecosystems. Ocean pollution which stems from various sources including plastic waste, chemical runoff, and overfishing has driven the international community to seek innovative solutions to reverse the tide of ecological degradation. In the midst of this challenge, the emergence of metaverse technologies, a convergence of augmented reality (AR), virtual reality (VR), blockchain, and other immersive digital realms, offer novel paradigms for tackling ocean pollution. The metaverse potential applications are as vast as the oceans themselves: from immersive marine conservation education to the monitoring of ocean health through digital twins, from incentivizing sustainable fishing practices through blockchain-based traceability to fostering cross-sector collaborations in ocean protection through immersive platforms. Legal-policy regulations form the bedrock upon which metaverse-driven changes can flourish. This chapter advocates for collaborative, interdisciplinary efforts, calling upon governments, industries, civil society, and academia to embrace innovation and legal adaptability as instruments of hope in safeguarding life below water and ensuring a sustainable future for generations to come.

Bhupinder Singh, Christian Kaunert

Marine Resources: Identification, Restoring, and Monitoring of Fisheries Food Resources Using Deep Learning and Image Processing

Abstract

Nature provides strength to people, and the ocean is one that is important. In the ocean, various resources are available to both nature and people. One such priceless resource is fish. Many varieties of fish are available in the sea. Seafood (fish and crab) is a healthy food for enhancing people’s health. But, nowadays so much offshore drilling for natural oil and gas production creates a huge destructive impact on marine resources, not only offshore due to other human mistakes spoiling the marine resources, especially fish. For this reason, we can save our marine resources with the help of new computer technology (artificial intelligence, deep learning, image processing). Aquaculture is one of the industries that currently frequently uses deep learning-based evaluation of images. Integrating an undersea time-lapse image camera with image analysis based on deep learning, we proposed this abstract for marine resources especially fish resources, first and foremost identify the fish, which means a variety of fish classified, then restoring the particular species, and eventually monitoring the movements under the sea. Optical cameras have been employed as a quick and affordable aquaculture technique for monitoring fish populations and detecting marine species. The photographs utilized in this research came from a variety of cameras; therefore, the pixels and size ratios varied. Additionally, it would be computationally expensive and ineffective to process the entire image. To eliminate the additional computational load, it was decided to crop the photographs to a uniform size. For millions of years, eating aquatic animals—especially fish—has been a key element of human nutrition. It is widely acknowledged that fish is an excellent provider of vital elements, such as proteins and vitamins. If the above technologies are utilized effectively, we can save our marine resources forever.

N. Nasurudeen Ahamed, Amreen Ayesha

Utilisation of Underwater Wireless Sensor Network Through Supervising a Random Network Environment in the Ocean Environment

Abstract

The oceans, which span more than 75% of the Earth’s surface, have the largest average water content and play an important part in the operation of our planet. However, because of the intricate and often elusive nature of activities at sea level, our understanding of these vast and enigmatic maritime regions is severely constrained. Underwater wireless sensors have emerged as critical instruments for bridging this information gap, capable of continually collecting and transmitting data about the physical and environmental state of the oceans. The phrase underwater wireless sensor network (UWSN) refers to the interconnected system produced by these modern underwater sensors, which are well known for their effectiveness in analysing a wide range of performance characteristics. This study looks at how changing two crucial factors—the maximum speed of node mobility and the total number of nodes—affects the performance of a random waypoint mobility model in the setting of underwater sensor networks. The research introduces the novel concept of a Random-Oriented Underwater Wireless Sensor Network (RO-UWSN) and conducts a thorough investigation, evaluating performance across various operational modes using key criteria such as average transmission delay, average jitter, average path loss and percentage of utilisation, paving the way for improved insights into oceanic dynamics and sensor network optimisation.

Sathish Kumar, C. V. Ravikumar, A. Srinivasulu, Tien Anh Tran

Role of Preprocessing Algorithm in the Underwater Image Analysis

Abstract

Various fields, such as marine biology, environmental monitoring, and underwater robots, all heavily rely on underwater image analysis. However, due to issues such as water turbidity, scattering, and light absorption, the quality of underwater photographs is greatly diminished, making conventional image processing approaches less efficient. To improve the quality and extract useful information from underwater photographs, preprocessing procedures must be used. The critical function of preprocessing techniques in underwater picture analysis is reviewed in this work.

This chapter discusses how important noise reduction and distortion removal techniques are when processing underwater images. The effectiveness of adaptive filtering, wavelet denoising, and other pertinent approaches is discussed to reduce the negative effects of noise and distortions produced during image acquisition.

This chapter explores how feature extraction, object recognition, and classification in underwater picture analysis are affected by pretreatment approaches. Preprocessing improves the effectiveness of these subsequent processes while also enabling a more thorough grasp of the undersea environment by enhancing the quality of input images.

Abhishek Choubey, Shruti Bhargava Choubey

Explainable AI for Ocean Health

Frontmatter

Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health

Abstract

The rapid integration of artificial intelligence (AI) into seas, ocean, and marine resource health has opened new opportunities for smart ocean health automation, transforming marine diagnosis, treatment, and the overall ocean ecosystem, thus contributing to Sustainable Development Goal 14 of ensuring sustainable and conserved use of seas, ocean, and other marine resources. However, the widespread adoption of AI algorithms in several domains, like human and ocean healthcare, comes with challenges, particularly regarding the transparency and explainability of these techniques. This study explores the explainable AI (XAI) concept and its crucial role in ocean healthcare automation. Discuss the significance of XAI, various techniques for achieving explainability, and their potential applications in ocean health. XAI can enhance accountability and facilitate better decision-making by enabling ocean, sea, and marine health professionals and the general population to understand and trust AI-driven ocean decisions. Moreover, the ethical considerations and challenges associated with implementing XAI in ocean healthcare settings, including privacy, bias, and regulatory implications, are addressed. Highlighted future directions in XAI research for smart ocean health and emphasized the implications for ocean health providers and policymakers. By embracing XAI, the ocean health industry can unlock the full potential of AI while ensuring transparency, fairness, and improved patient outcomes. It is revealed that ocean healthcare is most comparable to human healthcare. Therefore, it is paramount to have a collective responsibility to ensure the proper utilization and health of the ocean, marine resources, and seas.

Wasswa Shafik

Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health

Abstract

Deep learning models have undergone a significant transformation in terms of their understandability and transparency as a result of the introduction of Explainable Artificial Intelligence (XAI). Gaining insight into the internal workings of the XAI model enables researchers to verify its predictions and pinpoint possible areas for enhancement. Additionally, by facilitating more efficient dissemination of research results to a wider range of stakeholders, it increases trust in the accuracy of AI-powered evaluations of ocean health. In essence, the application of XAI ultimately improves the accountability and interpretability of AI models, making them more useful instruments for ocean monitoring researchers. This study investigates the application of Explainable Artificial Intelligence (XAI) in the context of assessing the condition of the ocean’s health. This study highlights the significance of Explainable Artificial Intelligence (XAI) and its capacity to address pertinent challenges. A deep learning-based CNN is utilized to training and test with sensor data for ocean anomaly detection. Results indicated lesser RMSE and greater values of precision which are signs of efficient prediction accuracy of the given model.

Sidra Tahir, Ehtesham Safeer, Faizan Ahmad

A Comprehensive Study of AI (XAI) for Ocean Health Monitoring

Abstract

Ocean ecosystem health is a critical part of the global ecosystem which directly or indirectly affects human health. In where, the Artificial Intelligence (AI) plays a transformative approach in monitoring the diseases and the health status of ocean organisms. Monitoring marine species is crucial due to the threats impacting on global climate changes and human activities like overfishing, ocean acidification, and hypoxic zones. In where, they can harm the ocean significantly and cause the rapid changes. Nowadays, detection in changes of environmental phenomena increasing data availability leads to expansion of sustainable ocean ecosystem health through the use of artificial intelligence AI and edge computing technology. Levering diverse data collected tools like sensors, satellites, underwater cameras, and acoustic devices enable the acquisition of extension data on environmental parameters, species presence and behavior. Moreover, the AI-driven systems are instrumental in overseeing environmental parameters like water quality, temperature, and nutrient levels. Through continuous monitoring, these AI algorithms offer the valuable insights into the well-being and the conditions of the observed ecosystem, facilitating the evaluation of ecosystem resilience, and the potential threats. Harnessing the capabilities of AI algorithms empowers the researchers and the conservationists to enhance their comprehension of marine ecosystems. Therefore, this study will facilitate the efficient management and conservation initiatives for the enduring health of ocean life and the preservation of our oceans.

Shreya Singh, Tien Anh Tran, Momina Shaheen

Explainable Artificial Intelligence for Ocean Health: Applications and Challenges

Abstract

As oceans around the globe confront enormous challenges related to the environment, the importance of artificial intelligence (AI) in understanding and maintaining ocean health is becoming more vital. This chapter explores the notion of “Explainable Artificial Intelligence for Ocean Health” and its importance in tackling complicated ocean-related concerns. Explainable AI (XAI) refers to AI systems that are meant to give clear, capable of interpretation, and human-understandable explanation for their opinions and forecasts. XAI is critical in bridging the gap between complicated AI models and usable findings for oceanographers, policymakers, and environmentalists in the context of ocean health. This chapter focuses on the three primary elements of XAI in the context of ocean health: environmental surveillance, prediction, and prediction of oceanic parameters such as temperatures, acidity, and pollution levels. Models like these enable scientists to detect and analyse crucial changes in ocean conditions by giving interpretable information. Predictive powers can aid in the prediction of undesirable occurrences such as toxic algal blooms or coral bleaching, allowing for prompt mitigation actions. Second, it focuses on Marine Species Conservation, explaining how the XAI can help in marine species conservation by allowing the study of massive information linked to biodiversity and ecological relationships. Researchers may use explainable AI algorithms to discover variables influencing the loss of certain species, follow migratory patterns, and estimate the effect of climate change on marine ecosystems. Finally, this chapter focuses on Policy Formulation and Decision Support, where XAI enables policymakers and stakeholders to reach informed decisions by providing transparent AI-driven insights. This is critical for developing efficient rules and regulations to protect ocean health.

In addition, by giving explicit rationale for funding decisions, XAI can ease allocation of resources for ocean conservation programmes. The use of Explainable Artificial Intelligence in the domain of ocean health is a game-changing method for solving the difficulties that our oceans confront. XAI not just improves our knowledge of marine systems by offering interpretable and actionable insights; however, it also helps us to take proactive efforts to protect these crucial ecosystems for future generations to come. This chapter emphasises XAI’s rising importance as a tool for ocean conservation and sustainable management.

Gnanasankaran Natarajan, Elakkiya Elango, Rakesh Gnanasekaran, Sandhya Soman

Edge Computing Based IoT for Ocean Health

Frontmatter

Revolutionizing Internet of Underwater Things with Federated Learning

Abstract

This chapter explores the transformative intersection of Federated Learning (FL) and the Internet of Underwater Things (IoUT), presenting a paradigm shift in how underwater things operate autonomously and efficiently. The unique challenges posed by the underwater environment necessitate innovative solutions, and FL emerges as a promising approach to address data privacy, resource constraints, and decentralized learning. The chapter delves into the integration of FL techniques, discussing their applications, benefits, and challenges in the context of IoUT. This chapter aims to contribute to the evolving literature on underwater drone technologies, providing a comprehensive overview of how Federated Learning can empower the Internet of Underwater Things to operate intelligently, autonomously, and securely in challenging underwater environments.

Momina Shaheen, Muhammad Shoaib Farooq, Tariq Umer, Tien Anh Tran

Federated Learning for Internet of Underwater Drone Things

Abstract

This chapter explores the transformative intersection of federated learning (FL) and the Internet of Underwater Drone Things (IoUDT) that come together to form a significant breakthrough that will have far-reaching effects on researchers who monitor ocean health. This integration tackles important issues specific to underwater environments by combining FL’s collaborative learning methodology with IoUDT’s underwater drone network. One important effect is the preservation of data privacy since FL permits decentralized learning while protecting sensitive data, which promote stakeholder trust. Furthermore, FL reduces communication barriers, boosts productivity, and allows drones to analyze data locally all of which contribute to optimal resource utilization. Underwater drone operations will undergo a paradigm shift as a result of this innovative method, which will transition from centralized control to decentralized autonomy and decision-making. Because of this, researchers are able to gather previously unheard-of knowledge about the health of the ocean, which helps them to make wise decisions for the preservation and conservation of marine environments. In the end, FL and IoUDT’s combination signals the beginning of a new chapter in ocean health monitoring, one that holds great promise for improvements in our knowledge of, ability to safeguard, and ability to manage the oceans sustainably.

Ehtesham Safeer, Sidra Tahir, Momina Shaheen, Muhammad Shoaib Farooq

Enhancing Underwater Imagery with AI/ML and IoT in ROV Technology

Abstract

Remotely operated vehicles (ROVs) enable underwater exploration and research but face imaging challenges from environmental conditions. This chapter examines how emerging technologies can enhance underwater imagery captured by ROVs. Specifically, the integration of artificial intelligence, machine learning (AI/ML), and Internet of Things (IoT) capabilities can automate and optimize image pre-processing, reduce noise, correct colors, and enable real-time analysis. Relevant algorithms and models are reviewed, including convolutional neural networks, color equalization, and utilization of RGB color proportions. This chapter begins by outlining the inherent environmental and technical challenges involved in capturing high-quality underwater images, particularly those obtained using remotely operated vehicles (ROVs). The discussion then shifts to the role of AI/ML in image pre-processing, with a specific focus on the models developed to improve image quality by reducing noise, adjusting color, and enhancing clarity. Various case studies are being carried out to examine the application of Internet of Things (IoT)-enabled sensors and cameras that are installed on remotely operated vehicles (ROVs), along with automated image pre-processing and real-time data transmission. This study showcases the practicality of these technologies in marine biology, underwater exploration, and environmental monitoring through a diverse range of illustrations. Furthermore, the chapter highlights the importance of maintaining a harmonious equilibrium between environmental and ethical factors when utilizing state-of-the-art underwater technology. This chapter concludes by presenting a perspective on future advancements and proposing recommendations for the optimal incorporation of AI/ML and the Internet of Things into ROV technology.

N. Chaithra, Janhvi Jha, Anu Sayal, M. Shravani Priya, Nithin Allagari, K. Chandana, Navya Aggarwal

Revolutionizing Ocean Cleanup: AI and Robotics Tackle Pollution Challenges

Abstract

The challenges posed by ocean pollution are many, and they demand our prompt attention along with innovative solutions. In this chapter, we will dive deeper into the Unmanned Ocean-Cleaning Robot for sustainable solution for ocean pollution check and cleaning purposes. This robotic technology holds the potential to mend the damage inflicted upon our seas by human activities and serves as a guardian for these vital ecosystems, ensuring their well-being for future generations to come. These issues stem from various sources, including plastic waste, chemical pollutants, and overfishing. To address these problems, we must alter our behavior, reduce our reliance on single-use plastics, adopt sustainable fishing practices, and develop better waste management systems. By doing so, we can collectively work toward cleaner, healthier oceans that support life on Earth. The Unmanned Ocean-Cleaning Robot is a testament to human ingenuity and our commitment to rectifying the harm we have caused to the seas. In the face of these challenges, it offers a glimmer of hope and a practical solution for a cleaner and more sustainable environment.

Divyansh Dadheech, Aditya Sunit Paul, Sonali Vyas, Akanksha Malakar

Review on the Optics and Photonics in Environmental Sustainability

Abstract

The rapid advancements in optics and photonics have provided innovative solutions to address various environmental challenges. This chapter presents a comprehensive review of the applications of optics and photonics in environmental sustainability. It explores the diverse range of technologies and methodologies that utilize light-based principles to monitor, mitigate, and manage environmental issues. The chapter covers key topics such as remote sensing, spectroscopy, photovoltaic, light-based water treatment, and optical sensors for environmental monitoring. Additionally, it discusses the potential of emerging technologies in optics and photonics to further enhance sustainability efforts. This aims to highlight the critical role of optics and photonics in shaping a more sustainable future and fostering a deeper understanding of their applications in environmental conservation and resource management.

Sunil Sharma, Sandip Das, Bhupendra Soni, Md. Sabir

Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission

Abstract

This paper presents a novel key exchange scheme based on the underwater acoustic channel that tackles the challenges posed by the uncertainty and vulnerability of the ocean environment. The proposed scheme models the channel’s uncertainty by constructing expressions for noise, multipath, and Doppler parameters and introducing the concept of underwater acoustic channel interference factors using Rényi entropy. To ensure identity authentication and initial key extraction, the scheme uses an intelligent hash function based on the twisted Edwards elliptic curve. It then employs the segmented initial key sequence to generate a segmented Toeplitz matrix, which is multiplied to generate labels through block operations, ensuring secure transmission of the initial key. The scheme enhances confidentiality through an additional hash process to generate the final security key. The scheme’s correctness, robustness, and confidentiality are confirmed using information theory, and simulation results show that it achieves a key generation rate of 631 bit/s with an upper bound of the adversary’s success rate of 4.3 × 10⁻²³ for an initial information volume of 50,000 bits, indicating significant advantages in terms of bit and bit error rates. Overall, this paper presents a promising key exchange scheme that can mitigate the challenges posed by the underwater acoustic channel’s uncertainty and vulnerability.

Mukesh Soni, Ismail Keshta, Renato R. Maaliw III, Shweta Singh, Pankaj Kumar

Artificial Intelligence as Key Enabler for Safeguarding the Marine Resources

Abstract

Artificial intelligence (AI) is a field within computer science dedicated to creating machine intelligence. It refers to the intelligence demonstrated by software or machines. In contemporary society, AI plays a pivotal role in supporting various aspects of our daily lives and economic activities. In the context of smart cities, an advanced urban development concept, data sensors, actuators, smart devices, and wireless communication systems are employed to generate and collect vast amount of data. The effective handling of this substantial data, known as big data, is crucial for optimizing smart city operations. AI emerges as a key technology for processing and learning from big data, leading to valuable insights that can enhance the efficiency and resourcefulness of smart cities. AI applications in smart cities extend to predicting failures, analyzing usage patterns, and forecasting resource and infrastructure demands. By leveraging AI capabilities, smart cities can become more adaptive, responsive, and sustainable, addressing the evolving needs of urban environments. The implementation of AI contributes to improved decision-making processes, resource allocation, and overall urban management. On the other hand, marine resources encompass both physical and biological entities found in oceans that are of utility to humans. The term gained prominence through Sustainable Development Goal 14, part of the United Nations’ 17 Sustainable Development Goals established in 2015. Goal 14 specifically focuses on “Life below water,” emphasizing the need to conserve and sustainably use oceans, seas, and marine resources for sustainable development.

Mehtab Alam, Ihtiram Raza Khan, Farheen Siddiqui, M. Afshar Alam

Correction to: Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health

Wasswa Shafik

Title: Artificial Intelligence and Edge Computing for Sustainable Ocean Health
Editors: Debashis De
Diganta Sengupta
Tien Anh Tran
Publisher: Springer Nature Switzerland
Electronic ISBN: 978-3-031-64642-3
Print ISBN: 978-3-031-64641-6
DOI: https://doi.org/10.1007/978-3-031-64642-3