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.

Precipitation Time Series Analysis and Forecasting for Italian Regions / Ghaderpour, Ebrahim; Dadkhah, Hanieh; Dabiri, Hamed; Bozzano, Francesca; SCARASCIA MUGNOZZA, Gabriele; Mazzanti, Paolo. - In: ENGINEERING PROCEEDINGS. - ISSN 2673-4591. - 39:1(2023). [10.3390/engproc2023039023]

Precipitation Time Series Analysis and Forecasting for Italian Regions

Ebrahim Ghaderpour
Primo
Writing – Original Draft Preparation
;
Hanieh Dadkhah
Writing – Review & Editing
;
Hamed Dabiri
Writing – Review & Editing
;
Francesca Bozzano
Writing – Review & Editing
;
Gabriele Scarascia Mugnozza
Writing – Review & Editing
;
Paolo Mazzanti
Ultimo
Writing – Review & Editing
2023

Abstract

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.
2023
ALLSSA; ARIMA; GPM; landslides; machine learning; Mann–Kendall; precipitation; remote sensing; time series forecasting; trend analysis
01 Pubblicazione su rivista::01a Articolo in rivista
Precipitation Time Series Analysis and Forecasting for Italian Regions / Ghaderpour, Ebrahim; Dadkhah, Hanieh; Dabiri, Hamed; Bozzano, Francesca; SCARASCIA MUGNOZZA, Gabriele; Mazzanti, Paolo. - In: ENGINEERING PROCEEDINGS. - ISSN 2673-4591. - 39:1(2023). [10.3390/engproc2023039023]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1685418
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