Artificial neural network approaches for disaster management: A literature review

Guha, Sreeparna; Jana, Rabin K.; Sanyal, Manas Kumar

doi:10.1016/j.ijdrr.2022.103276

Cited by 27 publications

(6 citation statements)

References 131 publications

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“…Artificial Intelligence (AI) has emerged as the oracle in natural disaster prediction, transforming our ability to anticipate and respond to environmental threats (Guha, Jana, and Sanyal, 2022). By analyzing historical data and real-time environmental measurements, AI models and early warning systems provide unprecedented accuracy in predicting natural disasters.…”

Section: Natural Disaster Prediction and Early Warning Systemsmentioning

confidence: 99%

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet

Onyebuchi Nneamaka Chisom,

Preye Winston Biu,

Aniekan Akpan Umoh

et al. 2024

World J. Adv. Res. Rev.

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The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions to preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring and conservation. This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction. AI's role in environmental monitoring and conservation includes wildlife tracking, habitat assessment, resource conservation, biodiversity analysis, and species identification. AI algorithms analyze camera trap footage, drone imagery, and GPS data to identify and estimate population sizes, leading to improved anti-poaching efforts and enhanced protection of diverse species. Habitat assessment and resource conservation involve AI-powered image analysis, which aids in assessing forest health, detecting deforestation, and identifying areas in need of restoration. Biodiversity analysis and species identification are achieved through AI algorithms that analyze acoustic recordings, environmental DNA (eDNA), and camera trap footage. These innovations identify different species, assess biodiversity levels, and even discover new or endangered species. AI-powered flood prediction systems provide early warnings, empowering communities with better preparedness and evacuation efforts. Challenges, such as data quality and availability, algorithmic bias, and infrastructure limitations, are acknowledged as opportunities for growth and improvement. In policy and regulation, the paper advocates for clear frameworks prioritizing data privacy and security, algorithmic transparency, and equitable access. Responsible development and ethical use of AI are emphasized as foundational pillars, ensuring that the integration of AI into environmental conservation aligns with principles of fairness, transparency, and societal benefit.

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Section: Natural Disaster Prediction and Early Warning Systemsmentioning

confidence: 99%

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet

Onyebuchi Nneamaka Chisom,

Preye Winston Biu,

Aniekan Akpan Umoh

et al. 2024

World J. Adv. Res. Rev.

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show abstract

“…They can extract relevant features from the images, such as cloud cover or precipitation, and use these features to predict the occurrence and intensity of natural disasters (Akter et al, 2021). It can learn spatial dependencies between the pixels in an image, allowing them to identify patterns and anomalies that may be indicative of an impending natural disaster and can be trained on large datasets, which can improve their accuracy and ability to generalize to new data (Guha et al, 2022) (Figure 7).…”

Section: Figurementioning

confidence: 99%

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

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Globally, communities and governments face growing challenges from an increase in natural disasters and worsening weather extremes. Precision in disaster preparation is crucial in responding to these issues. The revolutionary influence that machine learning algorithms have in strengthening catastrophe preparation and response systems is thoroughly explored in this paper. Beyond a basic summary, the findings of our study are striking and demonstrate the sophisticated powers of machine learning in forecasting a variety of weather patterns and anticipating a range of natural catastrophes, including heat waves, droughts, floods, hurricanes, and more. We get practical insights into the complexities of machine learning applications, which support the enhanced effectiveness of predictive models in disaster preparedness. The paper not only explains the theoretical foundations but also presents practical proof of the significant benefits that machine learning algorithms provide. As a result, our results open the door for governments, businesses, and people to make wise decisions. These accurate predictions of natural catastrophes and emerging weather patterns may be used to implement pre-emptive actions, eventually saving lives and reducing the severity of the damage.

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“…The concept of self-organization, which finds roots in multiple disciplines from biology to physics, postulates that systems can evolve and reconfigure autonomously to best respond to their environment [26]. This is achieved without explicit external commands or intervention, instead relying on inherent feedback mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Self-Organizing Control Systems for Nonlinear Spacecraft in the Class of Structurally Stable Mappings

Abdiramanov,

Taiman,

Beisenbi

et al. 2023

IJACSA

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In recent developments within the domain of aerospace engineering, there is a burgeoning interest in the autonomous control of nonlinear spacecraft using advanced methodologies. The present research delves deep into the realm of self-organizing control systems tailored for such nonlinear spacecraft, emphasizing its application within the framework of structurally stable mappings. By harnessing the inherent characteristics of structurally stable mappingsoften renowned for their resilience to minor perturbations and local modificationsthis research endeavors to design a control mechanism that mitigates the challenges presented by the intrinsic nonlinearity of spacecraft dynamics. Initial findings suggest a commendable enhancement in spacecraft maneuverability and robustness against unforeseen disturbances. Furthermore, the employment of self-organization principles leads to an adaptive and resilient system that can reconfigure its control strategies in real-time, basing decisions on immediate environmental feedback. This adaptability, in essence, mimics biological systems that evolve and adapt in the face of challenges. Such a breakthrough in nonlinear spacecraft control not only widens the horizons for space exploration by making missions safer and more efficient but also contributes foundational knowledge to the broader field of nonlinear dynamic system controls. Researchers and practitioners are encouraged to explore this synergistic combination of self-organization and structurally stable mappings to further harness its potential in diverse arenas beyond aerospace.

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Artificial neural network approaches for disaster management: A literature review

Cited by 27 publications

References 131 publications

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Self-Organizing Control Systems for Nonlinear Spacecraft in the Class of Structurally Stable Mappings

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