Shallow landslides, triggered by rainfall and affecting superficial deposits, pose significant dangers globally due to their potential for widespread occurrence, especially during intense rainfall episodes. To mitigate these risks, there's a crucial need for effective risk mitigation and early warning systems. Historically, shallow landslide warning systems have relied on rainfall intensity-duration thresholds, but recent advancements have introduced hydrometeorological thresholds, incorporating subsurface hydrological measurements. This study aims to develop and optimize hydrometeorological thresholds using hydrological and hydrometeorological monitoring data in the Oltrepo Pavese area of the Northern Italian Apennines, known for its susceptibility to landslides. Specifically, we assess the efficacy of the HydroMet statistical analysis tool in developing empirical thresholds for landslide initiation. HydroMet facilitates the statistical modeling process, enabling the development of objective empirical thresholds crucial for landslide prediction and mitigation strategies. By evaluating the performance and accuracy of the HydroMet model in capturing the complex relationships between hydrometeorological variables and landslide occurrences, we contribute to the advancement of landslide forecasting methodologies. The software application 'HydroMet' utilizes historical cumulative precipitation and saturation data to ascertain threshold values for landslide occurrences, incorporating soil hydrology factors to enhance prediction accuracy. Our objective is to develop a proactive early warning system that minimizes missed detections and false alarms, thereby serving as a robust foundation for future landslide warning systems. Leveraging advanced analytical techniques and comprehensive datasets, our framework aims to optimally balance sensitivity and specificity for timely and accurate identification of landslide events, ensuring enhanced reliability in landslide prediction.
Shallow landslides, triggered by rainfall and affecting superficial deposits, pose significant dangers globally due to their potential for widespread occurrence, especially during intense rainfall episodes. To mitigate these risks, there's a crucial need for effective risk mitigation and early warning systems. Historically, shallow landslide warning systems have relied on rainfall intensity-duration thresholds, but recent advancements have introduced hydrometeorological thresholds, incorporating subsurface hydrological measurements. This study aims to develop and optimize hydrometeorological thresholds using hydrological and hydrometeorological monitoring data in the Oltrepo Pavese area of the Northern Italian Apennines, known for its susceptibility to landslides. Specifically, we assess the efficacy of the HydroMet statistical analysis tool in developing empirical thresholds for landslide initiation. HydroMet facilitates the statistical modeling process, enabling the development of objective empirical thresholds crucial for landslide prediction and mitigation strategies. By evaluating the performance and accuracy of the HydroMet model in capturing the complex relationships between hydrometeorological variables and landslide occurrences, we contribute to the advancement of landslide forecasting methodologies. The software application 'HydroMet' utilizes historical cumulative precipitation and saturation data to ascertain threshold values for landslide occurrences, incorporating soil hydrology factors to enhance prediction accuracy. Our objective is to develop a proactive early warning system that minimizes missed detections and false alarms, thereby serving as a robust foundation for future landslide warning systems. Leveraging advanced analytical techniques and comprehensive datasets, our framework aims to optimally balance sensitivity and specificity for timely and accurate identification of landslide events, ensuring enhanced reliability in landslide prediction.
Sviluppo di soglie idrometeorologiche per la parte nord-orientale dell'area oltrepo pavese utilizzando il codice basato su Python HydroMet.
KHAN, SHAZAN
2022/2023
Abstract
Shallow landslides, triggered by rainfall and affecting superficial deposits, pose significant dangers globally due to their potential for widespread occurrence, especially during intense rainfall episodes. To mitigate these risks, there's a crucial need for effective risk mitigation and early warning systems. Historically, shallow landslide warning systems have relied on rainfall intensity-duration thresholds, but recent advancements have introduced hydrometeorological thresholds, incorporating subsurface hydrological measurements. This study aims to develop and optimize hydrometeorological thresholds using hydrological and hydrometeorological monitoring data in the Oltrepo Pavese area of the Northern Italian Apennines, known for its susceptibility to landslides. Specifically, we assess the efficacy of the HydroMet statistical analysis tool in developing empirical thresholds for landslide initiation. HydroMet facilitates the statistical modeling process, enabling the development of objective empirical thresholds crucial for landslide prediction and mitigation strategies. By evaluating the performance and accuracy of the HydroMet model in capturing the complex relationships between hydrometeorological variables and landslide occurrences, we contribute to the advancement of landslide forecasting methodologies. The software application 'HydroMet' utilizes historical cumulative precipitation and saturation data to ascertain threshold values for landslide occurrences, incorporating soil hydrology factors to enhance prediction accuracy. Our objective is to develop a proactive early warning system that minimizes missed detections and false alarms, thereby serving as a robust foundation for future landslide warning systems. Leveraging advanced analytical techniques and comprehensive datasets, our framework aims to optimally balance sensitivity and specificity for timely and accurate identification of landslide events, ensuring enhanced reliability in landslide prediction.È consentito all'utente scaricare e condividere i documenti disponibili a testo pieno in UNITESI UNIPV nel rispetto della licenza Creative Commons del tipo CC BY NC ND.
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https://hdl.handle.net/20.500.14239/17441