585 research outputs found
Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach
Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms
Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach
Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms
Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach
Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.Comment: 39 pages, 10 figure
RTS: road topology-based scheme for traffic condition estimation via vehicular crowdsensing
Urban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle-sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net into road sections and junction areas. Based on this division, we introduce a two-phased data collection and processing scheme named road topology-based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data.N/
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