A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

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Multi-scale flooding hazards evaluation using a nested flood simulation model: Case study of Jamuna River, Bangladesh

International Journal of River Basin Management ◽

10.1080/15715124.2021.1935977 ◽

2021 ◽

pp. 1-38

Author(s):

Masakazu Hashimoto ◽

Kenji Kawaike ◽

Tomonori Deguchi ◽

Shammi Haque ◽

Arpan Paul ◽

...

Keyword(s):

Simulation Model ◽

Model Case ◽

Flood Simulation ◽

Multi Scale ◽

Flooding Hazards

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A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Singapore Journal of Tropical Geography ◽

10.1111/sjtg.12126 ◽

2015 ◽

Vol 36 (3) ◽

pp. 308-323 ◽

Cited By ~ 5

Author(s):

Panchagnula Manjusree ◽

Chandra Mohan Bhatt ◽

Asiya Begum ◽

Goru Srinivasa Rao ◽

Veerubhotla Bhanumurthy

Keyword(s):

Data Analysis ◽

Satellite Data ◽

Flood Hazard ◽

North India ◽

Hazard Evaluation

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Spatial multi-criteria and multi-scale evaluation of walkability potential at street segment level: A case study of tehran

Sustainable Cities and Society ◽

10.1016/j.scs.2017.02.011 ◽

2017 ◽

Vol 31 ◽

pp. 37-50 ◽

Cited By ~ 18

Author(s):

Mohammad Taleai ◽

Elham Taheri Amiri

Keyword(s):

Street Segment ◽

Scale Evaluation ◽

Multi Scale

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Macroscopic and Multi-Scale Models for Multi-Class Vehicular Dynamics with Uneven Space Occupancy: A Case Study

Axioms ◽

10.3390/axioms10020102 ◽

2021 ◽

Vol 10 (2) ◽

pp. 102

Author(s):

Maya Briani ◽

Emiliano Cristiani ◽

Paolo Ranut

Keyword(s):

Road Network ◽

Real Data ◽

Second Order ◽

Heavy Vehicles ◽

First Order ◽

Multi Scale ◽

Stop And Go ◽

Scale Models ◽

Road Space

In this paper, we propose two models describing the dynamics of heavy and light vehicles on a road network, taking into account the interactions between the two classes. The models are tailored for two-lane highways where heavy vehicles cannot overtake. This means that heavy vehicles cannot saturate the whole road space, while light vehicles can. In these conditions, the creeping phenomenon can appear, i.e., one class of vehicles can proceed even if the other class has reached the maximal density. The first model we propose couples two first-order macroscopic LWR models, while the second model couples a second-order microscopic follow-the-leader model with a first-order macroscopic LWR model. Numerical results show that both models are able to catch some second-order (inertial) phenomena such as stop and go waves. Models are calibrated by means of real data measured by fixed sensors placed along the A4 Italian highway Trieste–Venice and its branches, provided by Autovie Venete S.p.A.

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