A Machine Learning Model Relating Xrain and Rain Gauge

Proceedings

article

Miao Zhang¹  Christopher Gomez¹  Balazs Bradak¹  Hotta Norifumi³  Shinohara Yoshinori⁴ 

[1] Sediment Hazards and Disaster Risk Laboratory, Graduate School of Oceanology, Kobe University;Faculty of Geography, Universitas Gadjah Mada;Faculty of Agriculture, The University of Tokyo;Faculty of Agriculture, University of Miyazaki

关键词: machine learning;    rainfall;    rainfall radar;    volcanic hazards;    lahars;   

DOI  :  10.3390/IECG2022-13828

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

In the last decade, rainfall radars have been deployed at volcanoes such as Mt. Merapi in Indonesia and can even cover a whole country such as in Japan, where the X-Rain (eXtended Radar Information network) product has been available for local research. However, the linkage between rain gauge data and spatial radar data (over a 250 m × 250 m grid) still presents discrepancies, and these challenges are particularly acute in regions of high local-topographic variations such as at Mount Unzen in Japan. As the volcano is located in the Shimabara peninsula, it is surrounded by the sea, with a topography locally rising to 1483 m. To improve the forecast and to better understand the triggering mechanisms of lahars (volcanic debris-flows) at Mount Unzen, quantifying the spatial distribution of rainfalls is essential, and first, it is important to understand how data taken locally by rain gages relate to spatial radar data. Because empirical models have not been able to show any clear correlation, the present contribution has been developing a neural network with two hidden layers that takes into account the rainfall per hour, the temperature and the wind speed and direction. The model takes a logistic activation function, and the loss function is optimized using the Mean Squared Errors and the Mean Absolute Error. The choice of the activation function and the optimizer is the result of running several combinations of optimization functions with different activation functions. Once the best fit was chosen, the sigmoid with a SGD (Stochastic Gradient Descent) optimizer was chosen, and when training the model for 120 cycles, Shimabara station and the Xrain data showed an error of <4 mm rainfall, while at the Unzen summit, even after 300 cycles, the validation error remained at 8 mm while the training loss was <4 mm. This shows that location specific functions might be necessary for each location, not only taking into account the weather data but also the local topographic variability and the topographic position on slopes.

【 授权许可】

CC BY