Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata

J, Litta A.; Idicula, Sumam Mary; Francis, Clovis

doi:10.5120/7819-1135

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Thunderstorm weather has the characteristics of quick movement and nonlinear changes; hence, accurate movement prediction of thunderstorm plays an important role in the basic theory of this research. Scholars such as Litta et al [4] and Collins and Tissot [5] believe that the artificial neural network (ANN) model is an effective modeling method for predicting the movement trend of thunderstorm clouds, which can predict the thunderstorm movement in this study. Ivanova [6], Yang et al [7], and other scholars analyzed the characteristics, movement, modeling methods, constraint conditions, and movement prediction ideas of thunderstorm weather based on historical sample data, demonstrating that 3D spatial modeling is reliable for thunderstorm weather prediction.…”

Section: State Of the Artmentioning

confidence: 89%

Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather

Lu,

Lai

2024

International Journal of Aerospace Engineering

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Thunderstorms are recognized as perilous meteorological phenomena characterized by irregular and nonlinear movement, posing significant risks to approaching aircraft and necessitating technical methods to ensure safety to the aviation operations. This research specifically addresses the challenges associated with aircraft during the approach segment and introduces a multialgorithmic model focusing on the optimization of 4D approach trajectory. Firstly, the artificial neural network intelligent model was used to predict the thunderstorm movement track. Secondly, the multialgorithmic model combined by the rapidly exploring random tree with artificial potential field was built to plan the trajectory of the approaching aircraft under thunderstorm weather, and then, the mean filter was adopted to smooth the simulated approaching trajectory. Finally, the reliability of the model with a real case study was demonstrated. After optimized simulation by predicting the thunderstorm weather and trajectory-optimized multialgorithmic model mentioned above, the approach trajectory can be outputted successfully, but with some distortions, postprocessing with the mean filter results in a remarkably smooth approach trajectory, providing enhanced feasibility and efficiency for pilots navigating through thunderstorm weather conditions. It is ultimately proved that refined 4D trajectory operations under hazardous weather conditions hold substantial significance in advancing aviation safety and operational effectiveness.

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Section: State Of the Artmentioning

confidence: 89%

Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather

Lu,

Lai

2024

International Journal of Aerospace Engineering

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“…The man-made errors that lead to forest fire are campfires that are not completely turned off, used and thrown mosquito coils, the smoked cigarettes remain, and tribal traditions related to fire. The natural causes that lead to forest fire are lightning [ 6 , 7 , 34 ], combustion of dry vegetation, and volcanic activities. UAV predicts the occurrence of forest fire based on any of the above-stated situations [ 35 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…e natural causes that lead to forest fire are lightning [6,7,34], combustion of dry vegetation, and volcanic activities. UAV predicts the occurrence of forest fire based on any of the above-stated situations [35].…”

Section: Prediction Of the Possibility Of Forestmentioning

confidence: 99%

Early Detection of Forest Fire Using Mixed Learning Techniques and UAV

Kasyap

Sumathi

Alluri

et al. 2022

Computational Intelligence and Neuroscience

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Over the last few decades, forest fires are increased due to deforestation and global warming. Many trees and animals in the forest are affected by forest fires. Technology can be efficiently utilized to solve this problem. Forest fire detection is inevitable for forest fire management. The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective. The mixed learning technique is composed of YOLOv4 tiny and LiDAR techniques. Unmanned aerial vehicles (UAVs) are promising options to patrol the forest by making them fly over the region. The proposed model deployed on an onboard UAV has achieved 1.24 seconds of classification time with an accuracy of 91% and an F1 score of 0.91. The onboard CPU is able to make a 3D model of the forest fire region and can transmit the data in real time to the ground station. The proposed model is trained on both dense and rainforests in detecting and predicting the chances of fire. The proposed model outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.

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“…Examples include general emergency incident prediction [22,26] or thunderstorm incident prediction [14,16,23].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Category of Fire Department Operations

Pirklbauer

Findling

2019

Proceedings of the 21st International Conference on Information Integration and Web-Based Applications &Amp; Services

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Voluntary fire departments have limited human and material resources. Machine learning aided prediction of fire department operation details can benefit their resource planning and distribution. While there is previous work on predicting certain aspects of operations within a given operation category, operation categories themselves have not been predicted yet. In this paper we propose an approach to fire department operation category prediction based on location, time, and weather information, and compare the performance of multiple machine learning models with cross validation. To evaluate our approach, we use two years of fire department data from Upper Austria, featuring 16.827 individual operations, and predict its major three operation categories. Preliminary results indicate a prediction accuracy of 61%. While this performance is already noticeably better than uninformed prediction (34% accuracy), we intend to further reduce the prediction error utilizing more sophisticated features and models. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Visualization; • Computing methodologies → Machine learning.

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Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata

Cited by 11 publications

References 19 publications

Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather

Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather

Early Detection of Forest Fire Using Mixed Learning Techniques and UAV

Predicting the Category of Fire Department Operations

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