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Artificial intelligence and IoT in water risk and water resources management (AlyVesi)

Current

AlyVesi -project using 5G and machine learning in real time (news, August 30, 2019)

Eagle Eyes -video

 
Invasive species mapping in Estonia 2019
Giant hogweed
Jattipalsami
Detecting balsam - invasive species
( Impatiens glandulifera) in the city of Oulu 2019.
Detecting giant hogweed with drones in Estonia 2019 (left).

 1. The purpose is to identify and evaluate the potential for machine learning and artificial intelligence in water resource management and water safety.

2. Machine learning and cloud services are being developed into new future services:

  • in hydrology (the state of the bed, the ice situation)
  • flood risk management & flood preparedness (water level)
  • environmental monitoring (aquatic vegetation, alien species) and
  • biomass assessments in water bodies.
Machine learning and ice movement.
Jäänlähtö
Classifying ice movement from images.

 

Drone a
 
nd machine vision services.
Biomassa_valokuvista
Biomassa evaluation with image recgonition (waterlily) .

 

IoT water temperature station

3. Deep learning in water level prediction - experiment.

4. Internet of Things (IoT) for monitoring surface and groundwater.

The aim is to evaluate the suitability of the new IoT technology for the various monitoring tasks in order to significantly reduce the cost and energy consumption of the device and the cost of data transmission and storage without compromising quality. The project will build, test and evaluate the functionality of IoT services at surface and groundwater stations. During the project, the stations will be connected to the IoT network and the functionality of the measurement device, data transmission networks and cloud information services will be evaluated and coordinated with existing monitoring systems.

More information

  • Jari Silander , Senior Researcher, Finnish Environment Institute SYKE, firstname.surname@ymparisto.fi
  • Teemu Heikkilä, CEO, Emblica Oy p. +358 400963509, teemu@emblica.fi
  • Jonas Stjernberg, CEO, BVdrone Oy p. +358 440 662 566, jonas@bvdrone.co
Rakkolanjoki_maski_neuroverkko
Rakkolanjoki camera and neural network.
Water_level_image_recgonition
Accuracy of water level reading with image recgontion.
Multispectral image for mapping invasives.Porvoo_drone_water_quality_mapping

 

Fixed wind drone mapping invasives 15.8.2019.
Drone water quality map (multispectral image - turbidity + chl-a) autumn 2019. .
Published 2019-02-28 at 14:00, updated 2020-05-07 at 12:58

Target group: