Research Article
Flashflood Hazard Assessment in Yewa South Lga
Issue:
Volume 10, Issue 2, April 2025
Pages:
49-59
Received:
1 February 2025
Accepted:
18 February 2025
Published:
7 March 2025
DOI:
10.11648/j.jccee.20251002.11
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Abstract: In the bid to accomplish the Sustainable Development Goals, several attempts have been made in Yewa South LGA to accomplish environmental sustainability (SDG7) and reduce the adverse effects of climate change (SDG13). The area has witnessed recurrent flash floods with deleterious effect to lives and properties due to anthropogenic factors coupled with incessant torrential rainfall events which are the major drivers of flood vulnerability in the area. Previous studies have adopted the use of GIS, Remote sensing or an integration both techniques with associated challenges. This study adopts the use of Hydrologic Engineering Centre’s Hydrologic Modelling System with Geographic Information Systems (HEC-GeoHMS) to evaluate the relationship between rainfalls, terrain characteristics, run off and stream flow as an alternative flood mitigation scheme. The catchment area was divided into forty-five sub basins over a 10m DEM, the run off hydrographs simulated and the hydrological characteristics modelled by using rainfall data between 1st June, 2022 – 31st May, 2023 as well as discharge data from Ogun-Osun River basin Development Authority (O-ORBDA). the model parameters were optimized for calibration and the calibrated model was thereafter validated using three statistical evaluation criteria which showed that there is a good simulation between the observed and estimated values (Rep = -2.24%, REv = 6.67%, NSE = 95.03%, and R2 = 0.83). Further analysis of the results showed that the flash flood is induced mainly by hydrologic characteristics of the area. This work therefore proposes to mitigate flood in Yewa South Local Government Area of Ogun State by modelling how excess water runs on the terrain thereby creating flash floods. The model will serve as an input for putting mitigation measures in place to arrest flash floods.
Abstract: In the bid to accomplish the Sustainable Development Goals, several attempts have been made in Yewa South LGA to accomplish environmental sustainability (SDG7) and reduce the adverse effects of climate change (SDG13). The area has witnessed recurrent flash floods with deleterious effect to lives and properties due to anthropogenic factors coupled w...
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Review Article
A Review on Aerospace-AI, with Ethics and Implications
Issue:
Volume 10, Issue 2, April 2025
Pages:
60-74
Received:
31 December 2024
Accepted:
3 February 2025
Published:
11 March 2025
DOI:
10.11648/j.jccee.20251002.12
Downloads:
Views:
Abstract: The rapid advancement of aerospace technology, coupled with the exponential growth in available data, has catalyzed the integration of artificial intelligence (AI) across the aerospace sector. This comprehensive review examines the state-of-the-art applications of AI, machine learning (ML), deep learning (DL), and generative artificial intelligence (GenAI) in aerospace. Our analysis reveals that ML algorithms demonstrate remarkable capabilities: Random forest (RF) algorithm achieves precision within 10 meters for trajectory prediction, while support vector machines (SVMs) algorithms show 99.89% accuracy in aircraft fault detection. Decision trees (DTs) algorithms excel in aircraft system diagnostics with adaptive learning capabilities. In the realm of deep learning, convolutional neural networks (CNNs) algorithms achieve 79% accuracy in satellite component detection and structural inspection, while recurrent neural networks (RNNs) algorithms and Long Short-Term Memory (LSTM) networks demonstrate superior performance in 4D trajectory prediction and engine health monitoring. GenAI, particularly through Generative adversarial networks (GANs), has revolutionized airfoil design optimization, achieving less than 1% error in profile fitting and 10% error in aerodynamic stealth characteristics. However, these algorithms face scalability challenges when processing large-scale datasets in real-time applications, particularly in mission-critical scenarios. Our research also identifies four ethical considerations, including bias prevention in automated systems, transparency in decision-making processes, privacy protection in data handling, and the implementation of important safety protocols. This study provides a foundation for understanding the current landscape of aerospace-AI integration while highlighting the importance of addressing ethical implications in future developments. The successful implementation of these technologies will require continuous innovation in validation methodologies, establish universal ethical considerations standard, and enhanced community engagement through citizen science initiatives to involve stakeholders.
Abstract: The rapid advancement of aerospace technology, coupled with the exponential growth in available data, has catalyzed the integration of artificial intelligence (AI) across the aerospace sector. This comprehensive review examines the state-of-the-art applications of AI, machine learning (ML), deep learning (DL), and generative artificial intelligence...
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,
Monsur Adewara
