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28 pages, 9861 KiB

Open AccessArticle

Optimal Fusion of Multispectral Optical and SAR Images for Flood Inundation Mapping through Explainable Deep Learning

by Jacob Sanderson, Hua Mao, Mohammed A. M. Abdullah, Raid Rafi Omar Al-Nima and Wai Lok Woo

Information 2023, 14(12), 660; https://doi.org/10.3390/info14120660 - 14 Dec 2023

Cited by 2 | Viewed by 2936

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A [...] Read more.

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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21 pages, 387 KiB

Open AccessArticle

The Coupling and Coordination Degree of Digital Business and Digital Governance in the Context of Sustainable Development

by Aleksy Kwilinski, Oleksii Lyulyov and Tetyana Pimonenko

Information 2023, 14(12), 651; https://doi.org/10.3390/info14120651 - 6 Dec 2023

Cited by 3 | Viewed by 2444

Abstract

The inexorable march of technological advancement, particularly within the digital domain, continues to exert a profound influence on global economies, societies, and governance frameworks. This paper delves into the intricate coordination between digital business and digital governance against the backdrop of sustainable development. [...] Read more.

The inexorable march of technological advancement, particularly within the digital domain, continues to exert a profound influence on global economies, societies, and governance frameworks. This paper delves into the intricate coordination between digital business and digital governance against the backdrop of sustainable development. By introducing an index system to gauge the levels of digital business and governance, this study assesses their coupling coordination using a coupling coordination model. Through this level of coordination, this paper assesses their respective contributions to the sustainable development objectives of EU countries through panel-corrected standard error (PCSE) estimates. The paper’s findings underscore several key conclusions: (1) Notable upswings are evident in the composite indices for digital business and digital governance growth. Among these, the index of digital business has demonstrated the most pronounced surge. Furthermore, digital business has experienced a distinct upward trajectory in recent years. (2) Although observable, the rise of the coupling degree is restrained, with an overall coupling degree that remains relatively low. The coupling progression has transitioned from a stage of low-degree coupling to that of primary coupling, with EU countries demonstrating fluctuating rising trends in their coupling degrees, marked by conspicuous regional disparities. (3) Over the examined period, the extent of coordination between digital business and digital governance substantially impacts the Sustainable Development Goals (SDG) index. Focusing on the interplay and harmonization between digital business and governance offers a novel pathway toward attaining the objectives of the Sustainable Development Goals. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

16 pages, 587 KiB

Open AccessArticle

Severity Grading and Early Detection of Alzheimer’s Disease through Transfer Learning

by Saeed Alqahtani, Ali Alqahtani, Mohamed A. Zohdy, Abdulaziz A. Alsulami and Subramaniam Ganesan

Information 2023, 14(12), 646; https://doi.org/10.3390/info14120646 - 3 Dec 2023

Viewed by 2083

Abstract

Alzheimer’s disease (AD) is an illness affecting the neurological system in people commonly aged 65 years and older. It is one of the leading causes of dementia and, subsequently, the cause of death as it gradually affects and destroys brain cells. In recent [...] Read more.

Alzheimer’s disease (AD) is an illness affecting the neurological system in people commonly aged 65 years and older. It is one of the leading causes of dementia and, subsequently, the cause of death as it gradually affects and destroys brain cells. In recent years, the detection of AD has been examined in ways to mitigate its impacts while considering early detection through computer-aided diagnosis (CAD) tools. In this study, we developed deep learning models that focus on early detection and classifying each case, non-demented, moderate-demented, mild-demented, and very-mild-demented, accordingly through transfer learning (TL); an AlexNet, ResNet-50, GoogleNet (InceptionV3), and SqueezeNet by utilizing magnetic resonance images (MRI) and the use of image augmentation. The acquired images, a total of 12,800 images and four classifications, had to go through a pre-processing phase to be balanced and fit the criteria of each model. Each of these proposed models split the data into 80% training and 20% testing. AlexNet performed an average accuracy of 98.05%, GoogleNet (InceptionV3) performed an average accuracy of 97.80%, and ResNet-50 had an average performing accuracy of 91.11%. The transfer learning approach assists when there is not adequate data to train a network from the start, which aids in tackling one of the major challenges faced when working with deep learning. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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16 pages, 6422 KiB

Open AccessArticle

RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning

by Sam Single, Saeid Iranmanesh and Raad Raad

Information 2023, 14(12), 633; https://doi.org/10.3390/info14120633 - 27 Nov 2023

Cited by 2 | Viewed by 6155

Abstract

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, [...] Read more.

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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13 pages, 2942 KiB

Open AccessArticle

Machine Learning in the Analysis of Carbon Dioxide Flow on a Site with Heterogeneous Vegetation

by Ekaterina Kulakova and Elena Muravyova

Information 2023, 14(11), 591; https://doi.org/10.3390/info14110591 - 1 Nov 2023

Viewed by 1629

Abstract

The article presents the results of studies of carbon dioxide flow in the territory of section No. 5 of the Eurasian Carbon Polygon (Russia, Republic of Bashkortostan). The gas analyzer Sniffer4D V2.0 (manufactured in Shenzhen, China) with an installed CO₂ sensor, quadrocopter [...] Read more.

The article presents the results of studies of carbon dioxide flow in the territory of section No. 5 of the Eurasian Carbon Polygon (Russia, Republic of Bashkortostan). The gas analyzer Sniffer4D V2.0 (manufactured in Shenzhen, China) with an installed CO₂ sensor, quadrocopter DJI MATRICE 300 RTK (manufactured in Shenzhen, China) were used as control devices. The studies were carried out on a clear autumn day in conditions of green vegetation and on a frosty November day with snow cover. Statistical characteristics of experimental data arrays are calculated. Studies of the influence of temperature, humidity of atmospheric air on the current value of CO₂ have been carried out. Graphs of the distribution of carbon dioxide concentration in the atmospheric air of section No. 5 on autumn and winter days were obtained. It has been established that when building a model of CO₂ in the air, the parameters of the process of deposition by green vegetation should be considered. It was found that in winter, an increase in air humidity contributes to a decrease in gas concentration. At an ambient temperature of 21 °C, an increase in humidity leads to an increase in the concentration of carbon dioxide. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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14 pages, 1737 KiB

Open AccessArticle

Artificial Intelligence Generative Tools and Conceptual Knowledge in Problem Solving in Chemistry

by Wajeeh Daher, Hussam Diab and Anwar Rayan

Information 2023, 14(7), 409; https://doi.org/10.3390/info14070409 - 16 Jul 2023

Cited by 16 | Viewed by 6229

Abstract

In recent years, artificial intelligence (AI) has emerged as a valuable resource for teaching and learning, and it has also shown promise as a tool to help solve problems. A tool that has gained attention in education is ChatGPT, which supports teaching and [...] Read more.

In recent years, artificial intelligence (AI) has emerged as a valuable resource for teaching and learning, and it has also shown promise as a tool to help solve problems. A tool that has gained attention in education is ChatGPT, which supports teaching and learning through AI. This research investigates the difficulties faced by ChatGPT in comprehending and responding to chemistry problems pertaining to the topic of Introduction to Material Science. By employing the theoretical framework proposed by Holme et al., encompassing categories such as transfer, depth, predict/explain, problem solving, and translate, we evaluate ChatGPT’s conceptual understanding difficulties. We presented ChatGPT with a set of thirty chemistry problems within the Introduction to Material Science domain and tasked it with generating solutions. Our findings indicated that ChatGPT encountered significant conceptual knowledge difficulties across various categories, with a notable emphasis on representations and depth, where difficulties in representations hindered effective knowledge transfer. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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16 pages, 5427 KiB

Open AccessArticle

Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing

by Saharsh Barve, Jody M. Webster and Rohitash Chandra

Information 2023, 14(7), 373; https://doi.org/10.3390/info14070373 - 30 Jun 2023

Cited by 4 | Viewed by 2461

Abstract

Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can [...] Read more.

Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia’s Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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20 pages, 1244 KiB

Open AccessArticle

An Informed Decision Support Framework from a Strategic Perspective in the Health Sector

by Mohammed Alojail, Mohanad Alturki and Surbhi Bhatia Khan

Information 2023, 14(7), 363; https://doi.org/10.3390/info14070363 - 26 Jun 2023

Cited by 5 | Viewed by 3315

Abstract

This paper introduces an informed decision support framework (IDSF) from a strategic perspective in the health sector, focusing on Saudi Arabia. The study addresses the existing challenges and gaps in decision-making processes within Saudi organizations, highlighting the need for proper systems and identifying [...] Read more.

This paper introduces an informed decision support framework (IDSF) from a strategic perspective in the health sector, focusing on Saudi Arabia. The study addresses the existing challenges and gaps in decision-making processes within Saudi organizations, highlighting the need for proper systems and identifying the loopholes that hinder informed decision making. The research aims to answer two key research questions: (1) how do decision makers ensure the accuracy of their decisions? and (2) what is the proper process to govern and control decision outcomes? To achieve these objectives, the research adopts a qualitative research approach, including an intensive literature review and interviews with decision makers in the Saudi health sector. The proposed IDSF fills the gap in the existing literature by providing a comprehensive and adaptable framework for decision making in Saudi organizations. The framework encompasses structured, semi-structured, and unstructured decisions, ensuring a thorough approach to informed decision making. It emphasizes the importance of integrating non-digital sources of information into the decision-making process, as well as considering factors that impact decision quality and accuracy. The study’s methodology involves data collection through interviews with decision makers, as well as the use of visualization tools to present and evaluate the results. The analysis of the collected data highlights the deficiencies in current decision-making practices and supports the development of the IDSF. The research findings demonstrate that the proposed framework outperforms existing approaches, offering improved accuracy and efficiency in decision making. Overall, this research paper contributes to the state of the art by introducing a novel IDSF specifically designed for the Saudi health sector. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence for Sustainable Development)

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