Predicting Landslides Using Locally Aligned Convolutional Neural Networks

Landslides, movement of soil and rock under the influence of gravity, are common phenomena that cause significant human and economic losses every year. Experts use heterogeneous features such as slope, elevation, land cover, lithology, rock age, and rock family to predict landslides. To work with such features, we adapted convolutional neural networks to consider relative spatial information for the prediction task. Traditional filters in these networks either have a fixed orientation or are rotationally invariant. Intuitively, the filters should orient uphill, but there is not enough data to learn the concept of uphill; instead, it can be provided as prior knowledge. We propose a model called Locally Aligned Convolutional Neural Network, LACNN, that follows the ground surface at multiple scales to predict possible landslide occurrence for a single point. To validate our method, we created a standardized dataset of georeferenced images consisting of the heterogeneous features as inputs, and compared our method to several baselines, including linear regression, a neural network, and a convolutional network, using log-likelihood error and Receiver Operating Characteristic curves on the test set. Our model achieves 2-7% improvement in terms of accuracy and 2-15% boost in terms of log likelihood compared to the other proposed baselines.Comment: Published in IJCAI 202

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N2O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N2O emissions from arable soils at regional or continental scales

CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.Comment: AAAI-21 Camera Read

3-D numerical modeling of methane hydrate deposits

Within the German gas hydrate initiative SUGAR, we have developed a new tool for predicting the formation of sub-seafloor gas hydrate deposits. For this purpose, a new 2D/3D module simulating the biogenic generation of methane from organic material and the formation of gas hydrates has been added to the petroleum systems modeling software package PetroMod®. T ypically, PetroMod® simulates the thermogenic generation of multiple hydrocarbon components including oil and gas, their migration through geological strata, and finally predicts the oil and gas accumulation in suitable reservoir formations. We have extended PetroMod® to simulate gas hydrate accumulations in marine and permafrost environments by the implementation of algorithms describing (1) the physical, thermodynamic, and kinetic properties of gas hydrates; and (2) a kinetic continuum model for the microbially mediated, low temperature degradation of particulate organic carbon in sediments. Additionally, the temporal and spatial resolutions of PetroMod® were increased in order to simulate processes on time scales of hundreds of years and within decimeters of spatial extension. As a first test case for validating and improving the abilities of the new hydrate module, the petroleum systems model of the Alaska North Slope developed by IES (currently Shlumberger) and the USGS has been chosen. In this area, gas hydrates have been drilled in several wells, and a field test for hydrate production is planned for 2011/2012. The results of the simulation runs in PetroMod® predicting the thickness of the gas hydrate stability field, the generation and migration of biogenic and thermogenic methane gas, and its accumulation as gas hydrates will be shown during the conference. The predicted distribution of gas hydrates will be discussed in comparison to recent gas hydrate findings in the Alaska North Slope region