Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

Abstract. Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslide–triggering thresholds. In this paper, we perform a quantitative analysis of the impacts that the uncertain knowledge of landslide initiation instants have on the assessment of landslide intensity–duration early warning thresholds. The analysis is based on an ideal synthetic database of rainfall and landslide data, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model. This dataset is then perturbed according to hypothetical reporting scenarios, that allow to simulate possible errors in landslide triggering instants, as derived from historical archives. The impact of these errors is analysed by combining different criteria to single-out rainfall events from a continuous series and different temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant. Errors influence thresholds in a way that they are generally underestimated. Potentially, the amount of the underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall, limits the possibility to set up links between thresholds and physio-geographical factors.

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Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-633-2018 ◽

2018 ◽

Vol 18 (2) ◽

pp. 633-646 ◽

Cited By ~ 25

Author(s):

David J. Peres ◽

Antonino Cancelliere ◽

Roberto Greco ◽

Thom A. Bogaard

Keyword(s):

Early Warning ◽

Continuous Series ◽

Uncertainty Sources ◽

Uncertain Knowledge ◽

Physically Based ◽

Landslide Early Warning ◽

Set Up ◽

The Impact ◽

Triggering Rainfall ◽

Landslide Triggering

Abstract. Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslide-triggering thresholds. In this paper, we perform a quantitative analysis of the impacts of uncertain knowledge of landslide initiation instants on the assessment of rainfall intensity–duration landslide early warning thresholds. The analysis is based on a synthetic database of rainfall and landslide information, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model, and is therefore error-free in terms of knowledge of triggering instants. This dataset is then perturbed according to hypothetical reporting scenarios that allow simulation of possible errors in landslide-triggering instants as retrieved from historical archives. The impact of these errors is analysed jointly using different criteria to single out rainfall events from a continuous series and two typical temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant, especially when errors exceed 1 day or the actual instants follow the erroneous ones. Errors generally lead to underestimated thresholds, i.e. lower than those that would be obtained from an error-free dataset. Potentially, the amount of the underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall limits the possibility to set up links between thresholds and physio-geographical factors.

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Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4913-2014 ◽

2014 ◽

Vol 18 (12) ◽

pp. 4913-4931 ◽

Cited By ~ 56

Author(s):

D. J. Peres ◽

A. Cancelliere

Keyword(s):

Monte Carlo ◽

Slope Stability ◽

Power Law ◽

Rainfall Intensity ◽

Antecedent Rainfall ◽

Rainfall Events ◽

Physically Based ◽

Power Law Equation ◽

Triggering Rainfall ◽

Landslide Triggering

Abstract. Assessment of landslide-triggering rainfall thresholds is useful for early warning in prone areas. In this paper, it is shown how stochastic rainfall models and hydrological and slope stability physically based models can be advantageously combined in a Monte Carlo simulation framework to generate virtually unlimited-length synthetic rainfall and related slope stability factor of safety data, exploiting the information contained in observed rainfall records and field-measurements of soil hydraulic and geotechnical parameters. The synthetic data set, dichotomized in triggering and non-triggering rainfall events, is analyzed by receiver operating characteristics (ROC) analysis to derive stochastic-input physically based thresholds that optimize the trade-off between correct and wrong predictions. Moreover, the specific modeling framework implemented in this work, based on hourly analysis, enables one to analyze the uncertainty related to variability of rainfall intensity within events and to past rainfall (antecedent rainfall). A specific focus is dedicated to the widely used power-law rainfall intensity–duration (I–D) thresholds. Results indicate that variability of intensity during rainfall events influences significantly rainfall intensity and duration associated with landslide triggering. Remarkably, when a time-variable rainfall-rate event is considered, the simulated triggering points may be separated with a very good approximation from the non-triggering ones by a I–D power-law equation, while a representation of rainfall as constant–intensity hyetographs globally leads to non-conservative results. This indicates that the I–D power-law equation is adequate to represent the triggering part due to transient infiltration produced by rainfall events of variable intensity and thus gives a physically based justification for this widely used threshold form, which provides results that are valid when landslide occurrence is mostly due to that part. These conditions are more likely to occur in hillslopes of low specific upslope contributing area, relatively high hydraulic conductivity and high critical wetness ratio. Otherwise, rainfall time history occurring before single rainfall events influences landslide triggering, determining whether a threshold based only on rainfall intensity and duration may be sufficient or it needs to be improved by the introduction of antecedent rainfall variables. Further analyses show that predictability of landslides decreases with soil depth, critical wetness ratio and the increase of vertical basal drainage (leakage) that occurs in the presence of a fractured bedrock.

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Probabilistic emission and immission modelling: case-study of the combined sewer – WWTP – receiving water system at Dessel (Belgium)

Water Science & Technology ◽

10.2166/wst.2002.0068 ◽

2002 ◽

Vol 45 (3) ◽

pp. 117-124 ◽

Cited By ~ 23

Author(s):

P. Willems ◽

J. Berlamont

Keyword(s):

Deterministic Model ◽

Water System ◽

Parameter Uncertainties ◽

Uncertainty Sources ◽

Probabilistic Simulation ◽

Deterministic Modelling ◽

Physically Based ◽

Input Uncertainties ◽

The Impact ◽

Receiving Water

The impact of the combined urban drainage and WWTP system of the village of Dessel (Belgium) on the Witte Nete receiving water is modelled both in terms of emissions and immissions. The hydrodynamic and water quality modelling is performed both in a deterministic and probabilistic way. For the deterministic modelling, detailed physically based and simplified conceptual models are used in a complementary way. In the probabilistic modelling, the different uncertainties in the deterministic model are classified in input uncertainties, parameter uncertainties and model-structure uncertainties. The probabilistic simulation results can be used in risk analysis and management, for the determination of the major uncertainty-sources and priorities in model improvement, for model bias elimination and for efficient model calibration.

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Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds

10.5194/hess-2017-308 ◽

2017 ◽

Cited By ~ 1

Author(s):

Francesco Marra ◽

Elisa Destro ◽

Efthymios I. Nikolopoulos ◽

Davide Zoccatelli ◽

Jean Dominique Creutin ◽

...

Keyword(s):

Debris Flow ◽

Early Warning ◽

Debris Flows ◽

Spatial Organization ◽

Radar Data ◽

Rain Gauge ◽

Spatial Aggregation ◽

Scale Parameters ◽

The Impact ◽

Triggering Rainfall

Abstract. The systematic underestimation observed in debris flows early warning thresholds has been associated to the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine resolution radar data for ~ 100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1–20-km grid size) on the estimation of debris flow triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show up to 8 % and 21 % variations in the shape and scale parameters respectively. Thresholds from synthetic rain gauge networks show > 10 % variation in the shape and > 25 % systematic underestimation in the scale parameter, even for densities as high as 1/10 km−2.

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Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4525-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4525-4532 ◽

Cited By ~ 31

Author(s):

Francesco Marra ◽

Elisa Destro ◽

Efthymios I. Nikolopoulos ◽

Davide Zoccatelli ◽

Jean Dominique Creutin ◽

...

Keyword(s):

Debris Flow ◽

Early Warning ◽

Spatial Organization ◽

Radar Data ◽

Rain Gauge ◽

Spatial Aggregation ◽

Rain Gauges ◽

Fine Resolution ◽

The Impact ◽

Triggering Rainfall

Abstract. The systematic underestimation observed in debris flow early warning thresholds has been associated with the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris-flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine-resolution radar data for ∼ 100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1–20 km grid size) on the estimation of debris-flow-triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show 8–21 % variation in the parameters whereas 10–25 % systematic variation results from the use of rain gauge networks, even for densities as high as 1∕10 km−2.

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Sensitivity analysis and calibration of a dynamic physically-based slope stability model

10.5194/nhess-2017-73 ◽

2017 ◽

Author(s):

Thomas Zieher ◽

Martin Rutzinger ◽

Barbara Schneider-Muntau ◽

Frank Perzl ◽

David Leidinger ◽

...

Keyword(s):

Sensitivity Analysis ◽

Slope Stability ◽

Laboratory Tests ◽

Rainfall Event ◽

Model Ensemble ◽

Rainfall Events ◽

Stability Model ◽

Physically Based ◽

Triggering Rainfall ◽

Landslide Triggering

Abstract. Physically-based modelling of slope stability at catchment scale is still a challenging task. Applying a physically-based model at such scale (1 : 10,000 to 1 : 50,000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) if parameters cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically-based slope stability models. In the present study a dynamic physically-based coupled hydrological/geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multi-temporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, effective angle of internal friction, effective cohesion) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.5 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak runoff increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

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Resilient Scheduling as a Response to Uncertainty in Construction Projects

Applied Sciences ◽

10.3390/app11146493 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6493

Author(s):

Martina Milat ◽

Snježana Knezić ◽

Jelena Sedlar

Keyword(s):

Construction Projects ◽

Dynamic Environment ◽

Simulation Method ◽

Project Planning ◽

Project Duration ◽

Uncertainty Sources ◽

Probabilistic Simulation ◽

Project Deliverables ◽

Negative Impacts ◽

The Impact

Complex construction projects are developed in a dynamic environment, where uncertainty conditions have a great potential to affect project deliverables. In an attempt to efficiently deal with the negative impacts of uncertainty, resilient baseline schedules are produced to improve the probability of reaching project goals, such as respecting the due date and reaching the expected profit. Prior to introducing the resilient scheduling procedure, a taxonomy model was built to account for uncertainty sources in construction projects. Thence, a multi-objective optimization model is presented to manage the impact of uncertainty. This approach can be described as a complex trade-off analysis between three important features of a construction project: duration, stability, and profit. The result of the suggested procedure is presented in a form of a resilient baseline schedule, so the ability of a schedule to absorb uncertain perturbations is improved. The proposed optimization problem is illustrated on the example project network, along which the probabilistic simulation method was used to validate the results of the scheduling process in uncertain conditions. The proposed resilient scheduling approach leads to more accurate forecasting, so the project planning calculations are accepted with increased confidence levels.

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Sensitivity analysis and calibration of a dynamic physically based slope stability model

Natural Hazards and Earth System Science ◽

10.5194/nhess-17-971-2017 ◽

2017 ◽

Vol 17 (6) ◽

pp. 971-992 ◽

Cited By ~ 10

Author(s):

Thomas Zieher ◽

Martin Rutzinger ◽

Barbara Schneider-Muntau ◽

Frank Perzl ◽

David Leidinger ◽

...

Keyword(s):

Sensitivity Analysis ◽

Slope Stability ◽

Laboratory Tests ◽

Rainfall Event ◽

Model Ensemble ◽

Rainfall Events ◽

Stability Model ◽

Physically Based ◽

Triggering Rainfall ◽

Landslide Triggering

Abstract. Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological–geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

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CYBERBULLYING SEBAGAI DAMPAK NEGATIF PENGGUNAAN TEKNOLOGI INFORMASI

Jurnal Sistem Informasi ◽

10.21609/jsi.v8i1.321 ◽

2013 ◽

Vol 8 (1) ◽

pp. 22 ◽

Cited By ~ 1

Author(s):

Flourensia Sapty Rahayu

Keyword(s):

Information Technology ◽

High School Students ◽

Technology Development ◽

Negative Impact ◽

School Students ◽

Negative Impacts ◽

Set Up ◽

A Minor ◽

The Impact ◽

Insight Into

Information Technology can bring postive and negative impacts to our lives. One of the negative impact that emerge with the this technology development is Cyberbullying. Cyberbullying is any cyber-communication or publication posted or sent by a minor online, by Information Technology devices that is intended to frighten, embarrass, harass, hurt, set up, cause harm to, extort, or otherwise target another minor. In other countries there are many cases of Cyberbullying that ended with very serious event such as the suicide of the victims. This study was conducted to gain insight into how this phenomenon occur in Indonesia. We used questionnaires as a mean to get the informations about Cyberbullying among Indonesian teenagers. We distributed these questionnaires to secondary and high school students in Magelang, Yogyakarta and Semarang. The result shows that Cyberbullying has already happened with a big enough number (28%) but the impact was not very serious. From the answers we can conclude that many teens haven’t understand what Cyberbullying is and what its potential dangerous impacts may follow. We also explored the roles, responsibilities, and things that can be done by teens, parents, schools, law enforcements, and communities in order to prevent and stop Cyberbullying.

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