Related papers: Forecasting landslides using community detection o…
Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. Physics-based detailed hydrological and hydraulic computational tools, such as HEC-RAS, MIKE, and…
Natural landslides exhibit scaling properties revealed by power law relationships. These relationships include the frequency of the size (e.g., area, volume) of the landslides, and the rainfall conditions responsible for slope failures in a…
Mobile multi-robot teams deployed for monitoring or search-and-rescue missions in urban disaster areas can greatly improve the quality of vital data collected on-site. Analysis of such data can identify hazards and save lives.…
Climate change and increases in drought conditions affect the lives of many and are closely tied to global agricultural output and livestock production. This research presents a novel approach utilizing machine learning frameworks for…
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for atmospheric density forecasting are vital for space…
We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development…
The success of deep learning techniques over the last decades has opened up a new avenue of research for weather forecasting. Here, we take the novel approach of using a neural network to predict full probability density functions at each…
Satellite-derived data products and climate model simulations of geophysical variables like precipitation, often exhibit systematic biases compared to in-situ measurements. Bias correction and spatial downscaling are fundamental components…
Crop type classification using satellite observations is an important tool for providing insights about planted area and enabling estimates of crop condition and yield, especially within the growing season when uncertainties around these…
Weather forecasting is essential for facilitating diverse socio-economic activity and environmental conservation initiatives. Deep learning techniques are increasingly being explored as complementary approaches to Numerical Weather…
Precipitation is a key part of hydrological circulation and is a sensitive indicator of climate change. The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) datasets are widely used for…
Study Region: Goslar and G\"ottingen, Lower Saxony, Germany. Study Focus: In July 2017, the cities of Goslar and G\"ottingen experienced severe flood events characterized by short warning time of only 20 minutes, resulting in extensive…
Monitoring long-term landslide activity is important for risk assessment and land management. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited when separating landslides…
The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management. Most of the mapping and tracking efforts are manual which is…
This paper develops a novel method, based on hidden Markov models, to forecast earthquakes and applies the method to mainshock seismic activity in southern California and western Nevada. The forecasts are of the probability of a mainshock…
The sea level observations from satellite altimetry are characterised by a sparse spatial and temporal coverage. For this reason, along-track data are routinely interpolated into daily grids. The latter are strongly smoothed in time and…
Advancements in remote sensing have led to development of several satellite-derived precipitation products; however, their accuracy must be evaluated before use in scientific and operational studies. This study comprehensively assesses six…
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from…
Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of…
Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard…