Precipitation Time Series Analysis and Forecasting for Italian Regions

Engineering Proceedings

article

Ebrahim Ghaderpour¹  Hanieh Dadkhah¹  Hamed Dabiri¹  Francesca Bozzano¹  Gabriele Scarascia Mugnozza¹  Paolo Mazzanti¹ 

[1] Department of Earth Sciences & CERI Research Centre, Sapienza University of Rome;NHAZCA s.r.l.

关键词: ALLSSA;    ARIMA;    GPM;    landslides;    machine learning;    Mann–Kendall;    precipitation;    remote sensing;    time series forecasting;    trend analysis;   

DOI  :  10.3390/engproc2023039023

来源: mdpi

【 摘 要 】

In Italy, most of the destructive landslides are triggered by rainfall, particularly in central Italy. Therefore, effective monitoring of rainfall is crucial in hazard management and ecosystem assessment. Global precipitation measurement (GPM) is the next-generation satellite mission, which provides the precipitation measurements worldwide. In this research, we employed the available monthly GPM data to estimate the monthly precipitation for the twenty administrative regions of Italy from June 2000 to June 2021. For each region, we applied the non-parametric Mann–Kendall test and its associated Sen’s slope to estimate the precipitation trend for each calendar month. In addition, for each region, we estimated a linear trend and the seasonal cycles of precipitation with the antileakage least-squares spectral analysis (ALLSSA) and showed the annual precipitation variations using box plots. Lastly, we compared machine-learning models based on the auto-regressive moving average for monthly precipitation forecasting and showed that ALLSSA outperformed them. The findings of this research provide a significant insight into processing climate data, both in terms of trend-season estimates and forecasting, and can potentially be used in landslide susceptibility analysis.

【 授权许可】

Unknown