Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

2018 ◽  
Vol 32 (15) ◽  
pp. 5041-5052 ◽  
Author(s):  
Georgios N. Kouziokas ◽  
Alexander Chatzigeorgiou ◽  
Konstantinos Perakis
Keyword(s):  
Groundwater Level ◽  
Public Management ◽  
Meteorological Data ◽  
Forward Models ◽  
Feed Forward

scholarly journals Realtime monitoring for groundwater level and local climate based on universal communication system

2021 ◽  
Vol 2 (2) ◽  
pp. 67-76
Author(s):  
Rony Teguh ◽  
Hepryandi Usup
Keyword(s):  
Real Time ◽  
Groundwater Level ◽  
Meteorological Data ◽  
Current Method ◽  
Local Climate ◽  
Climate Conditions ◽  
Weather Patterns ◽  
The People ◽  
Universal Communication

The groundwater level and weather patterns and climate conditions are several of the very significant factors which influence the quality of livelihood and the other activity of the tropical peatland environment. The current method of groundwater level and meteorological information aggregate build the use of certain expensive weather station devices, prominent to a lack of vast monitoring suitable to cost barriers and disturbance in some countries. In this research, we have developed and implemented a hardware module based on an Arduino microcontroller and mobile communication, which measures the groundwater level and meteorological data, including air temperature, air humidity, and soil temperature, and humidity, rainfall in peatland area. The data groundwater level is received by a specially developed application interface running on an Internet of Things (IoT) connected through a Global Mobile System (GSM) communication. In this work, our proposed system is a model system that can able to generate alerts based on the real-time groundwater level and data weather as potential peat fire in Indonesia. It provides online and data real-time monitoring. In this works, we have resulted in a system to monitor the groundwater level and data weather alert, condition mapping, and warn the people from its disastrous effects.

scholarly journals Spatial-temporal variation of groundwater and land subsidence evolution in Beijing area

2015 ◽  
Vol 372 ◽  
pp. 7-11
Author(s):  
K. Lei ◽  
Y. Luo ◽  
B. Chen ◽  
M. Guo ◽  
G. Guo ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Meteorological Data ◽  
Rapid Expansion ◽  
Persistent Scatterer Interferometry ◽  
Systematic Analysis ◽  
Groundwater Levels ◽  
Level Data ◽  
Regional Land

Abstract. Precipitation is the main recharge source of groundwater in the plain of Beijing, China. Rapid expansion of urbanization has resulted in increased built-up area and decreased amount of effective recharge of precipitation to groundwater, indirectly leading to the long-term over-exploitation of groundwater, and induced regional land subsidence. Based on the combination of meteorological data, groundwater level data, interferometric synthetic aperture radar (InSAR; specifically persistent scatterer interferometry, PSI), geographic information system (GIS) spatial analysis method and rainfall recharge theory, this paper presents a systematic analysis of spatial-temporal variation of groundwater level and land subsidence evolution. Results show that rainfall has been decreasing annually, while the exploitation of groundwater is increasing and the groundwater level is declining, which is has caused the formation and evolution of land subsidence. Seasonal and interannual variations exist in the evolution of land subsidence; the subsidence is uneven in both spatial and temporal distribution. In 2011, at the center of mapped subsidence the subsidence rate was greater than 120 mm a−1. The results revealed good correlation between the spatial distribution of groundwater level declines and subsidence. The research results show that it is beneficial to measure the evolution of land subsidence to dynamic variations of groundwater levels by combining InSAR or PSI, groundwater-level data, and GIS. This apprpach provides improved information for environmental and hydrogeologic research and a scientific basis for regional land subsidence control.

Automated Feed-Forward Learning for Pressure-Compensated Mobile Hydraulic Valves With Significant Dead-Zone

2017 ◽  
Author(s):  
Jarmo Nurmi ◽  
Jouni Mattila
Keyword(s):  
Motion Control ◽  
Dead Zone ◽  
Heavy Duty ◽  
Automated Identification ◽  
Forward Models ◽  
Feed Forward ◽  
Loop Control ◽  
Hydraulic Manipulators ◽  
Hydraulic Manipulator ◽  
Hydraulic Valves

Hydraulic manipulators on mobile machines, whose hydraulic actuators are usually controlled by mobile hydraulic valves, are being considered for robotic closed-loop control. A feed-forward-based strategy combining position and velocity feedback has been found to be an effective method for the motion control of pressure-compensated mobile hydraulic valves that have a significant dead zone. The feed-forward can be manually identified. However, manually identifying the feed-forward models for each valve-actuator pair is often very time-consuming and error-prone. For this practical reason, we propose an automated feed-forward learning method based on velocity and position feedback. We present experimental results for a heavy-duty hydraulic manipulator on a forest forwarder to demonstrate the effectiveness of the proposed method. These results motivate the automated identification of velocity feed-forward models for motion control of heavy-duty hydraulic manipulators controlled by pressure-compensated mobile hydraulic valves that have a significant input dead zone.

scholarly journals Twelve-picture RSVP sequences support feed-forward models of detection at 75 Hz

2013 ◽  
Vol 13 (9) ◽  
pp. 1047-1047
Author(s):  
M. C. Potter ◽  
C. E. Hagmann ◽  
B. Wyble
Keyword(s):  
Forward Models ◽  
Feed Forward

scholarly journals Editorial: Neuro-motor control and feed-forward models of locomotion in humans

2015 ◽  
Vol 9 ◽  
Author(s):  
Marco Iosa ◽  
Leonardo Gizzi ◽  
Federica Tamburella ◽  
Nadia Dominici
Keyword(s):  
Motor Control ◽  
Forward Models ◽  
Feed Forward

Riparian zone hydrological rehabilitation along the Gyöngyös stream (Hungary)

2021 ◽  
Author(s):  
Előd Szőke ◽  
Péter Csáki ◽  
Péter Kutschi ◽  
Péter Kalicz ◽  
Zoltán Gribovszki
Keyword(s):  
Water Supply ◽  
Groundwater Level ◽  
Riparian Forest ◽  
Meteorological Data ◽  
Riparian Ecosystems ◽  
Monitoring Wells ◽  
Double Mass ◽  
Relationship Of ◽  
The Impact ◽  
Positive Effect

<p>Climate change induced drought periods are likely to cause decline in groundwater level,<br>which can degrade riparian ecosystems (such as riparian forest). With a reasonable water<br>supply, water scarcity can be stopped and these valuable ecosystems can be preserved.</p><p>The aim of the research was to evaluate the impact of water supply interventions regarding<br>habitat reconstruction of Doroszló meadows near Kőszeg (west Hungary). Groundwater<br>monitoring wells have been installed at 4 representative sites of the area. Groundwater wells<br>were 3–5 m deep and screened at their bottoms (2–4 m). The water level of the wells was<br>recorded manually, on a weekly basis, with an accuracy of 1 mm. In the neighborhood of the<br>wells surface close soil moisture values were also measured. Data from April 2019 to<br>October 2020 were collected. Local meteorological data measured in Kőszeg were also<br>used for analysis.</p><p>Evaluating the data from each well in the pre-intervention period (the analysis of the<br>relationship of the wells with the control well), we came to the conclusion that the Well-1 and<br>Well-2 behave similarly. The impacts of the water supply on the groundwater level were<br>analysed using a “double mass curve” and a “treatment-control space-time deviations”<br>approach. Result showed that the intervention had a positive effect only on the Well-3 from<br>the examined wells. The data evaluation denoted that unfortunately the control well was also<br>affected by water supply interventions.</p><p>This research has been supported by the Ministry of Agriculture in Hungary.</p>

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