To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June⁻12 July 2017. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. 10 Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 9 bbe Moldaenke GmbH, Preetzer Chaussee 177, 24222 Schwentinental, Germany. 8 bbe Moldaenke GmbH, Preetzer Chaussee 177, 24222 Schwentinental, Germany. 7 Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 6 Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 5 Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark.
4 Institute of Applied Geoscience, Karlsruhe Institute of Technology, Kaiserstr. 3 Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 2 Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr. 1 Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Kaiserstr.The product suite comprises MIKE HYDRO River for river applications, MIKE FLOOD for surface water flooding, MIKE SHE for integrated catchment hydrology, MIKE HYDRO Basin for water resources planning and MIKE 21C for river sediments and morphology applications. Whether your projects focus on water resources planning, river hydraulics, groundwater, flooding, sediment transport, ecology, screening analyses, or detail studies – we have the tools you need. Our water resources products are preferred by more professionals around the world than any similar products. Our products work individually or in combination to assist our clients to make optimal and well-balanced decisions in solving today’s water resources challenges.
R getdata inland water software#
Our unique software suite embeds DHI’s industry leading expertise in a range of highly specialised products covering all aspects of inland water dynamics and water resources availability and quality. Successful water resources management is becoming an increasingly complex and challenging task with issues ranging from drought and water scarcity to severe flood incidents.