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Estimating meteorological visibility range under foggy weather conditions: A deep learning approach

机译：估算有雾天气条件下的气象可视性范围：深度学习方法

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Hazar Chaabani; Naoufel Werghi; Faouzi Kamoun; Bilal Taha; Fatma Outay; Ansar-Ul-Haque Yasar;
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1. Estimating meteorological visibility range under foggy weather conditions: A deep learning approach [J] . Hazar Chaabani, Naoufel Werghi, Faouzi Kamoun, Procedia Computer Science . 2018,第5期

机译：估算有雾天气条件下的气象可视性范围：深度学习方法
2. A Neural network approach to visibility range estimation under foggy weather conditions [J] . Hazar Chaabani, Faouzi Kamoun, Hichem Bargaoui, Procedia Computer Science . 2017,第期

机译：有雾天气条件下可见范围估算的神经网络方法
3. Gradient flow-based deep residual networks for enhancing visibility of scenery images degraded by foggy weather conditions [J] . Suganya R., Kanagavalli R. Journal of ambient intelligence and humanized computing . 2021,第1期

机译：基于渐变流的深度残余网络，用于提高风景图像的可见性，通过有雾的天气状况降级
4. Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach [C] . Da Zhang, Mansur R. Kabuka 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing, 2017 IEEE Cyber Science and Technology Congress . 2017

机译：结合天气状况数据来预测交通流量：基于GRU的深度学习方法
5. A Targeted Approach to Provide Weather Guidance for General Aviation Pilots Based on Estimated Time of Departure and Personal Weather Minimums [D] . Dennstaedt, Scott C. 2021

机译：基于估计的出发时间和个人天气最低限度，提供了一般航空飞行员天气指导的目标方法
6. The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China [O] . Hujia Zhao, Huizheng Che, Yanjun Ma, 2017

机译：东北多城市雾/有雾条件下PM变化与能见度和混合层高度的关系
7. A Neural network approach to visibility range estimation under foggy weather conditions [O] . Hazar Chaabani, Faouzi Kamoun, Hichem Bargaoui, 2017

机译：有雾天气条件下可见范围估算的神经网络方法

Estimating meteorological visibility range under foggy weather conditions: A deep learning approach

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