A5066952511- Soil Geostatistics and Mapping
- Spatial and Panel Data Analysis
- Hydrology and Drought Analysis
- Climate variability and models
- Innovative Approaches in Technology and Social Development
- Meteorological Phenomena and Simulations
- Land Use and Ecosystem Services
- Scientific Computing and Data Management
- Hydrology and Watershed Management Studies
- Semantic Web and Ontologies
- Advanced Computational Techniques and Applications
- Financial Risk and Volatility Modeling
- Advanced Database Systems and Queries
- Soil Moisture and Remote Sensing
- Air Quality Monitoring and Forecasting
- Vehicle emissions and performance
- Big Data Technologies and Applications
- Hydraulic flow and structures
- Data Management and Algorithms
- Traffic Prediction and Management Techniques
- Geochemistry and Geologic Mapping
- Hydrological Forecasting Using AI
- Mineral Processing and Grinding
- Geographic Information Systems Studies
- Distributed and Parallel Computing Systems
52°North Spatial Information Research
2017-2022
Ruhr University Bochum
2016-2017
Norsk Hydro (Germany)
2016-2017
University of Münster
2013-2016
We present new spatio-temporal geostatistical modelling and interpolation capabilities of the R package gstat.Various covariance models have been implemented, such as separable, product-sum, metric sum-metric models.In a real-world application we compare spatiotemporal interpolations using these with purely spatial kriging approach.The target variable is daily mean PM 10 concentration measured at rural air quality monitoring stations across Germany in 2005.R code for variogram fitting...
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent cross-validation residuals, indicates that predictions maybe biased, this suboptimal. This paper presents a random for framework (RFsp) where buffer distances from observation as explanatory variables, thus incorporating geographical proximity effects into...
Abstract. Most of the hydrological and hydraulic studies refer to notion a return period quantify design variables. When dealing with multiple variables, well-known univariate statistical analysis is no longer satisfactory, several issues challenge practitioner. How should one incorporate dependence between variables? multivariate be defined applied in order yield proper event? In this study an overview state art for estimating events given different approaches are compared. The construction...
Combined Global Surface Summary of Day and European Climate Assessment Dataset daily meteorological data sets (around 9000 stations) were used to build spatio‐temporal geostatistical models predict air temperature at ground resolution 1 km for the global land mass. Predictions in space time made mean, maximum, minimum temperatures using regression‐kriging with a series Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model wetness...
Studying phenomena that follow a skewed distribution and entail an extremal behaviour is important in many disciplines. How to describe model the dependence of spatial random fields still challenging question. Especially when one interested interpolating sample from field exhibits extreme events, classical geostatistical tools like kriging relying on Gaussian assumption fail reproducing extremes. Originating multivariate value theory partly driven by financial mathematics, copulas emerged...
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent cross-validation residuals, indicates that predictions maybe biased, this suboptimal. This paper presents a random for framework (RFsp) where buffer distances from observation as explanatory variables, thus incorporating geographical proximity effects into...
Copulas are a flexible tool to model dependence of random variables. They cover the range from perfect negative positive dependence, include independent case and incorporate asymmetric as well widely used Gaussian structure. The pair-copula construction for multivariate copulas exploits ease bivariate suggests decomposition copula into set ones. We successfully adapted this approach spatial data developed powerful based interpolation method.
Our environment is characterized by a changing climate marked rapidly increasing frequency and intensity of extreme weather leading to compound multi-hazard events. This evolving reality accentuates diverse needs across various sectors, as each grapples with unique vulnerabilities adaptation requirements. Stakeholders, ranging from individuals, local communities governmental bodies private enterprises, need take measures mitigate these challenges. These heterogeneous ask for...
The Linked Brazilian Amazon Rainforest Data contains observations about deforestation of rainforests and related things such as rivers, road networks, population, amount cattle, market prices agricultural products. approach
Maintaining knowledge about the provenance of datasets, that is, how they were obtained, is crucial for their further use. Contrary to what overused metaphors 'data mining' and 'big data' are implying, it hardly possible use data in a meaningful way if information sources types conversions discarded process gathering. A generative model spatiotemporal could not only help automating description derivation processes but also assessing scope dataset's future by exploring transformations. Even...
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent cross-validation residuals, indicates that predictions maybe biased, this suboptimal. This paper presents a random for framework (RFsp) where buffer distances from observation as explanatory variables, thus incorporating geographical proximity effects into...
Novel sensor technologies are rapidly emerging. They enable a monitoring and modelling of our environment in level detail that was not possible few years ago. However, while the raw data produced by these sensors useful to get first overview, it usually needs be post-processed integrated with other or models different applications. In this paper, we present an approach for integrating several geoprocessing components TaMIS water dam system developed Wupperverband, regional waterbody...
Abstract. Despite considerable efforts and progress in increasing resilience to natural hazards, the adverse socio-economic impacts of extreme weather events continue increase globally. As climate change progresses, disaster risk management needs alignment with adaptation measures. In this perspective paper, we discuss emerging complications during recent from an interoperability perspective. We argue that a lack between data models, information communication, governance are barriers...
Abstract. Most of the hydrological and hydraulic studies refer to notion a return period quantify design variables. When dealing with multiple variables, well-known univariate statistical analysis is no longer satisfactory several issues challenge practitioner. How should one incorporate dependence between variables? joint be defined applied? In this study, an overview state-of-the-art for defining periods given. The construction multivariate distribution functions done through use copulas,...
Abstract. Many hydrological studies are devoted to the identification of events that expected occur on average within a certain time span. While this topic is well established in univariate case, recent advances focus multivariate characterization based copulas. Following previous study, we show how definition survival Kendall return period fits into set periods.Moreover, preliminary investigate ability definitions select maximal from series. Starting rich simulated data set, similar...
During the last decades, global and regional climate models have been widely used for estimation of future conditions. Unfortunately, models’ estimated values present important biases relative to observed values, especially when estimations refer extremes. Consequently, several researchers studied statistical methods that are able minimize between values. The study evaluates a new method bias correction: triangular irregular network (TIN)-copula method. This is combination networks copula...
Abstract In climatology, there is a clear need for more reliable data, especially in regions where no meteorological stations exist. Different statistical methods as well regional climate models are usually used covering areas with limited data. However, important biases between real and simulated parameters observed, respect to extremes. The present study introduces new method that combines triangular irregular networks copulas the simulation of extreme maximum minimum temperatures....
Given the global scope of current climate crisis, it is important that be addressed in all sectors society. From increased risk extreme weather events, to heightened variability patterns, data and knowledge sharing among both citizens scientists alike necessary for planning a sustainable future. Thus, I-CISK project aims create human-centered, co-designed, co-created, co-implemented, co-evaluated service (CS), which allows citizens, stakeholders, decision-makers take climate-informed...
Climate change is a pressing issue that affects countries and communities around the world. As global temperatures intermittently, so do occurrences intensities of extreme weather events: which creates compounding, sometimes simultaneous, instances disasters. Thus, it evident there an urgent need for improved paradigms within Disaster Risk Management (DRM) climate adaptation (CCA) domains, to promote better risk assessment, governance, communication, systems prevent, respond, disaster...
Worldwide around 1.35 million people died in traffic accidents 2016 and up to 50 were injured [1]. While much research has already been conducted accident prevention measures have installed the emerge of new data technologies recent years broadened potential in-depth prediction. In this article, we present an overview about relevant city Bremen, Germany, assess how these can be used understand predict risks.