Twitter is increasingly being used as a real-time human-sensor network during natural disasters, detecting, tracking and documenting events. Current wildfire models currently largely omit social media data, representing a shortcoming in current models, as valuable and timely information is transmitted via this channel. By including this data as a real-time data source, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This monitoring model is coupled to a real-time forecasting of wildfire dynamics.

Real-time forecasting of wildfire dynamics, which has attracted increasing attention recently in fire safety science, is extremely challenging due to the complexities of the physical models and the geographical features. Running physics-based simulations for large-scale wildfires can be computationally difficult. We propose a novel algorithm scheme, which combines reduced-order modelling (ROM), recurrent neural networks (RNN), data assimilation (DA) and error covariance tuning for real-time forecasting/monitoring of the burned area. An operating cellular automata (CA) simulator is used to compute a data-driven surrogate model for forecasting fire diffusions. A long-short-term-memory (LSTM) neural network is used to build sequence-to-sequence predictions following the simulation results projected/encoded in a reduced-order latent space. 

We implement machine learning in a wildfire prediction model, using social media and geophysical data sources with sentiment analysis to predict wildfire instances and characteristics with high accuracy. The geophysical data is satellite data provided by the Global Fire Atlas, and social data is provided by Twitter. In doing this, we perform our own data collection and analysis, comparing regional differences in online social sentiment expression.

The performance of the proposed algorithm has been tested in recent massive wildfire events in California.

How to cite: Lever, J., Cheng, S., and Arcucci, R.: Social & Physics Based Data Driven Methods for Wildfire Prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15645, https://doi.org/10.5194/egusphere-egu23-15645, 2023.

The fragile geomorphological context of Italy sets a variety of natural challenges, ranging from seismic to hydrogeological risk. In such a complex territory, documenting the conditions of infrastructures is crucial for planning adequate strategies of maintenance through 3D modelling for structural analysis and digital twins’ implementation of structures like dams (Pagliari et al., 2016) or bridges (Gaspari et al., 2022). Geomatics, through periodical surveys using state-of-the-art technologies, reconstruct accurate 3D models of structures that results in the generation of dense pointclouds from which polygon meshes can be derived as well as in the model integration in Building Information Modeling (BIM) or Finite Element Method (FEM) environments for the computation of simulations and deformation monitoring or structural health assessment analysis in support of decision making.

Such data are generated through different approaches. A traditional methodology first implies the materialization and measurement of a topographic network in a local system with a total station and its subsequent georeferencing in a global coordinate reference system through a roto-translation based on Global Navigation Satellite System observations of ground control points. In the same framework, scans for the acquisition of dense pointclouds are defined through the adoption of a terrestrial laser scanner (TLS). Hence, the execution of planned drone flights, with nadiral and side view of the structure and its surrounding environment, serving as input for the generation of photogrammetric cloud through a robust Structure from Motion data processing.

Implementing open-source WebGL solutions like Potree supports the digital twin and data sharing with audiences of different technical backgrounds, committers concerned with the adoption of a monitoring platform for integrating products in different format as well as experts with non-geomatics expertise interested in further analysis of collected data through computer vision and deep learning approches that enrich the existent documentation. With a user-friendly interactive web platforms users are able to access the 3D model, make measurements and execute simple processing operation like cross-sections and clipping (e.g. https://labmgf.dica.polimi.it/piacenzacs/lugagnano/).

Since 2019, the dams of the Sila mountains in the Calabria region represented the case study for testing the described integrated approach. The present work concerns the integration of data from different sensors (TLS for indoor and outdoor environment, photogrammetric images and lidar from drone) for the generation of the digital twin of the arcuate-plan gravity dam of Trepidò. The dam digital twin of the dam and adjacencies consists of a pointcloud of 2594370 points, with adaptive density and average accuracy of 1-2 cm for the structure and 10 cm for the downstream vegetated sediment. It can be used to increase knowledge of the structure (built in 1930) and for structural analysis.

Bibliography:

Gaspari, F., Ioli, F., Barbieri, F., Belcore, E., and Pinto, L. (2022): Integration of UAV-LiDAR and UAV-photogrammetry for infrastructure monitoring and bridge assessment, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 995–1002, doi.org/10.5194/isprs-archives-XLIII-B2-2022-995-2022.

Pagliari, D., Rossi, L., Passoni, D., Pinto, L., de Michele, C., and Avanzi, F. (2016). Measuring the volume of flushed sediments in a reservoir using multi-temporal images acquired with UAS, Geomatics, Natural Hazards and Risk, 8(1), 150–166, doi.org/10.1080/19475705.2016.1188423

How to cite: Gaspari, F., Barbieri, F., Ioli, F., Pinto, L., and Valgoi, P.: Integration of 3D surveying approaches for critical infrastructure digital twins in natural hazard-prone scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7773, https://doi.org/10.5194/egusphere-egu23-7773, 2023.

Most science and engineering problems are modeled by time-dependent and parametrized nonlinear partial differential equations. Their resolution with traditional computational methods may be too expensive, especially in the context of predictions with uncertainty quantification or optimization, to allow for rapid predictions.  In this talk, we will overview data-driven methods aimed at representing high-fidelity computational models by means of reduced-dimension surrogate ones.  Different approaches will be presented for the uncertainty quantification for reliable predictions and forecasts in inundation problems.

Particularly, a non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale physical problems. The method uses two-level autoencoders to reduce the spatial and temporal dimensions from a set of high-fidelity snapshots collected from an in-house high-fidelity numerical solver of the shallow-water equations. The encoded latent vectors, generated from two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron. The accuracy of the proposed approach is compared to the linear reduced-order technique-based artificial neural network (POD-ANN) on benchmark tests (the Burgers and Stoker's solutions) and a hypothetical dam-break flow problem over a complex bathymetry river. The numerical results show that the proposed nonlinear framework presents strong predictive abilities to accurately approximate the statistical moments of the outputs for complex stochastic large-scale and time-dependent problems, with low computational cost during the predictive online stage.

The caveat that remains is the long-term temporal extrapolation for problems marked by sharp gradients and discontinuities. Our study explores forecasting convolutional architectures (LSTM, TCN, and CNN) to obtain accurate solutions for time-steps distant from the training domain, on advection-dominated test cases. A simple convolutional architecture is then proposed and shown to provide accurate results for the forecasts. To evaluate the epistemic uncertainties in the solutions, the methodology of deep ensembles is adopted.

REFERENCES

• Bhatt, Y. Kumar and A. Soulaïmani. Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems, DOI: 10.48550/arXiv.2209.09651.
• Abdedou and A. Soulaïmani. Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders.
  arXiv:2208.03190[physics.flu-dyn].
• Jacquier, A. Abdedou, V. Delmas and A. Soulaimani. Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling. Journal of Computational Physics. Volume 424, 1 January 2021, 109854.
• Abdedou and A. Soulaïmani. A non-intrusive reduced-order modeling for uncertainty propagation of time-dependent problems using a B-splines Bézier elements-based method and proper orthogonal decomposition: Application to dam-break flows. Computers & Mathematics with Applications. Volume 102, 15 November 2021, Pages 187-205.
• Chaudhry and A. Soulaimani. A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process. Appl. Sci. 2022, 12(5), 2324; https://doi.org/10.3390/app12052324.
• Delmas and A. Soulaimani. Parallel high-order resolution of the Shallow-water equations on real large-scale meshes with complex bathymetries. Journal of Computational Physics. Volume 471, 15 December 2022, 111629

How to cite: Soulaïmani, A., Abdedou, A., Kumar, Y., and Bhatt, P.: Deep Convolutional Architectures for Uncertainty Quantification and Forecast in Inundation Problems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-39, https://doi.org/10.5194/egusphere-egu23-39, 2023.

Floods are one of the most devastating natural disasters in the world causing loss of human lives and property across the world. These losses can be minimized by accurate prediction of floods well in advance. However, 2D hydrodynamic models which are used for flood inundation modelling require high computational time and hence are unsuitable for development of real-time flood monitoring system in most cases. Therefore, a surrogate machine learning model named XGBoost Regressor (XBGR) is developed for flood inundation modelling. The developed model overcomes the constraint of high computational time required by 2D hydrodynamic models. The XGBR is developed to predict maximum flood depth map and is evaluated with the LISFLOOD-FP hydrodynamic model. The training data for the XGBR model is generated using the LISFLOOD-FP model. The surrogate model is trained on 21 flood events, tested on 4 and validated for 1 flood event. For better development of the surrogate model, physical characteristics of the study area are considered in the form of nine indices referred here as topographic variables along with the flood characteristic variables. However, to refrain the XGBR model from overfitting and decrease the training time, a feed forward feature selection method is used to select the best predictive topographic variables. Four topographic variables are selected after which there is no significant improvement in the model was found. Number of trees and learning rate parameters of XGBR model are parameterized which are having highest impact on the model performance. Mean absolute error (MAE) and root mean square error (RMSE) are used for evaluating model accuracy. For testing period, the average MAE and RMSE are 0.433 m and 0.780 m, respectively and for the validation event MAE and RMSE are 0.595 m and 0.960 m respectively. For evaluating the accuracy of the surrogate model on flood inundation extent, F1 score is used which is the harmonic mean of precision and recall. The F1 score is 0.908 for the testing events and is 0.931 for validation events. The higher value of F1 score (>0.9) indicates good accuracy of the XGBR model when validated using the hydrodynamic model.

How to cite: Sasanapuri, S. K., Chadrika Thulaseedharan, D., and Ashwini Kumar, G.: Using machine learning to emulate the hydrodynamic model for flood inundation modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-554, https://doi.org/10.5194/egusphere-egu23-554, 2023.

Early warning systems protect and support lives, jobs, land and infrastructure. DAKI-FWS, a German national project, aims at developing an early warning system to protect the German society and economy against extreme weather and climate events such as floods, droughts and heatwaves. With a seasonal temporal horizon, DAKI-FWS requires high resolution and bias corrected seasonal forecast of daily minimum and maximum temperatures, daily precipitation and wind speed. To derive such information, we have developed a deep neural network (DNN) approach to downscale and bias correct coarse resolution seasonal forecast ensembles on a 1 degree grid to a 1 arc minute grid.

The proposed DNN approach is here analyzed and compared with other machine learning approaches. Results show that such a deep learning technique can generate realistic, temporally consistent, and high-resolution climate information. The statistical and physical properties of the generated ensembles are analyzed using spatial correlation, cross validation and SVD. The DNN predicts extreme values that are very close to the observed values while preserving the physical relationships in the system as well as the trends in the variables.

How to cite: Heidari, F., Lin, Q., Espitia Sarmiento, E. F., Toreti, A., and Xoplaki, E.: Towards the development of an AI-based early warning system: a deep learning approach to bias correct and downscale seasonal climate forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9555, https://doi.org/10.5194/egusphere-egu23-9555, 2023.

The present paper focuses on the influence of Rayleigh and Love waves on the seismic structural performance of a simplified nonlinear beam structure representing a bridge column. The impact of surface waves in the structure is quantified directly by a coupled 3D SEM-FEM numerical wave
propagation simulation from the earthquake source to the structure using the Domain Reduction Method.
In the first step, ground motions, including basin-induced surface waves, are generated from a regional model containing the earthquake source and a simplified basin. Surface waves are extracted and characterized with the Normalized Inner Product (NIP) in terms of amplitude and
frequency content from ground motions at different locations inside the basin. In the second step, the seismic wavefield from the SE simulation is imposed in a FE model composed of a nonlinear structure placed over a portion of the basin sediments. The model considers soil-structure
interaction and structural non-linearity through a multifiber beam approach.
By placing the structure in different positions, the extracted surface waves and the structural damage can be linked to a specific location inside the basin. Therefore, the spatial variability of the structural damage and the surface wave characteristics can be quantified. Consequently, this work
evaluates if structural damage can be estimated only from typical ground motion intensity parameters or if other parameters associated with surface wave characteristics are necessary. The results show a correlation between obtained seismic damage with rotational components from
surface waves (torsion for Love waves and rocking for Rayleigh waves).

How to cite: Soto, V. and Lopez-Caballero, F.: Quantification of source- and basin-induced surface waves effects on the seismic performance of nonlinear structures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11611, https://doi.org/10.5194/egusphere-egu23-11611, 2023.

Usually, the earthquake catalog for a given region represents a collection of all detected and localized earthquakes and, thus, contains not only the main shocks, but also fore- and aftershocks. In order to perform an independent seismic event and seismic hazard analysis we require a catalog that, ideally, contains only mainshocks. Thus, the removal of dependent fore- and aftershocks from an earthquake catalogby declustering is a crucial step in seismic hazard analysis. Machine learning methods can potentially offer improvements in speed and accuracy in comparison to classical declustering approaches.

Here, we propose a hybrid approach to identify the temporal clusters of earthquakes from the catalogs of California (USGS) and Japan (ISC). We combine unsupervised 1-D clustering algorithms with seismologically informed methods and machine learning techniques. We use epidemic type aftershock sequence (ETAS) generated catalogs as well as classically declustered catalogs to benchmark the method.

How to cite: Köhler, J., Li, W., Faber, J., Rümpker, G., Stöcker, H., and Srivastava, N.: A hybrid approach for declustering of earthquake catalogs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11462, https://doi.org/10.5194/egusphere-egu23-11462, 2023.