Prediction of groundwater level fluctuations in steady and unsteady state conditions using PMWIN in Rayen Aquifer, Iran

2019 ◽  
Vol 151 ◽  
pp. 414-424
Author(s):  
Fatemeh Amiri ◽  
Gholam-abbas Barani ◽  
Masoud Reza Hessami Kermani ◽  
Aida Tayebiyan ◽  
Rasoul Sabahi ◽  
...  
Keyword(s):  
Groundwater Level ◽  
Unsteady State ◽  
Groundwater Level Fluctuations

scholarly journals Investigation of Sentinel-1 Derived Land Subsidence Using Wavelet Tools And Triple Exponential Smoothing Algorithm In Lagos, Nigeria

2021 ◽  
Author(s):  
Femi Emmanuel Ikuemonisan ◽  
Vitalis Chidi Ozebo ◽  
Olawale Babatunde Olatinsu
Keyword(s):  
Soil Moisture ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Exponential Smoothing ◽  
Future Trend ◽  
Smoothing Algorithm ◽  
Groundwater Abstraction ◽  
Groundwater Level Fluctuations ◽  
History Of ◽  
Sbas Insar

Abstract Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate private borehole and water well proliferation. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series is computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between –94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm which is approximately 23% increase.

scholarly journals STUDY OF THE PROGRESS OF SEAWATER INTRUSION WITHIN A PLAIN AREA OF RHODOPE PREFECTURE

2004 ◽  
Vol 36 (4) ◽  
pp. 2057 ◽  
Author(s):  
Φ. Πλιάκας ◽  
I. Διαμαντής ◽  
A. Καλλιώρας ◽  
Χ. Πεταλάς
Keyword(s):  
Electrical Conductivity ◽  
Grain Size ◽  
Seawater Intrusion ◽  
Groundwater Level ◽  
Specific Electrical Conductivity ◽  
Chemical Analyses ◽  
Conductivity Measurements ◽  
Groundwater Level Fluctuations ◽  
Plain Area ◽  
Groundwater Samples

This paper investigates the progress of seawater intrusion within the plain area of Xylagani - Imeros, in SW part of Rhodope Prefecture, as well as the suitability of groundwater for several purposes, after qualitative valuation of groundwater samples from selective wells of the study area. The conclusions also include some managerial suggestions for the confrontation of seawater intrusion. The investigation in question took place between 1994-1997 and 2002-2003, and involves the installation of piezometric wells, geoelectric sounding measurements, grain size analyses, monitoring of the groundwater level fluctuations in selective wells, specific electrical conductivity measurements and chemical analyses of water samples from selective wells of the study area.

scholarly journals Analysis of long-term observations of the groundwater level In an aseismic region

2019 ◽  
pp. 47-67
Author(s):  
A. A. Lyubushin ◽  
O. S. Kazantseva ◽  
A. B. Manukin
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Atmospheric Pressure ◽  
Active Regions ◽  
Sampling Interval ◽  
Pressure Effects ◽  
Adaptive Compensation ◽  
Groundwater Level Fluctuations ◽  
And Cluster Analysis

The results of the analysis of continuous precise time series of atmospheric pressure and groundwater level fluctuations in a well drilled to a depth of 400 m in the territory of Moscow are presented. The observations are remarkable in terms of their duration of more than 22 years (from February 2, 1993 to April 4, 2015) and by the sampling interval of 10 min. These long observations are suitable for exploring the stationarity of the properties of hydrogeological time series in a seismically quiet region, which is important from the methodological standpoint for interpreting the similar observations in seismically active regions aimed at earthquake prediction. Factor and cluster analysis applied to the sequence of multivariate vectors ofthe statistical properties of groundwater level time series in the successive 10-day windows after adaptive compensation for atmospheric pressure effects distinguish five different statistically significant states of the time series with the transitions between them. An attempt to geophysically interpret the revealed states is made. Two significant periods – 46 and 275 days – are established by spectral analysis of the sequence of the transitions times between the clusters.

scholarly journals On fractal nature of groundwater level fluctuations due to rainfall process

2016 ◽  
Vol 82 ◽  
pp. 103-115 ◽  
Author(s):  
Maminirina Joelson ◽  
Jacques Golder ◽  
Philippe Beltrame ◽  
Marie-Christine Néel ◽  
Liliana Di Pietro
Keyword(s):  
Groundwater Level ◽  
Fractal Nature ◽  
Groundwater Level Fluctuations ◽  
Rainfall Process

Modeling of groundwater level fluctuations in agricultural monitoring sites

2011 ◽  
pp. 217-224
Author(s):  
V. Vircavs ◽  
V. Jansons ◽  
A. Veinbergs ◽  
K. Abramenko ◽  
Z. Dimanta ◽  
...  
Keyword(s):  
Groundwater Level ◽  
Groundwater Level Fluctuations

scholarly journals The Role of Environmental Background Processes in Determining Groundwater Level Variability—An Investigation of a Record Flood Event Using Dynamic Factor Analysis

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2336
Author(s):  
Balázs Trásy ◽  
Norbert Magyar ◽  
Tímea Havril ◽  
József Kovács ◽  
Tamás Garamhegyi
Keyword(s):  
Factor Analysis ◽  
Groundwater Level ◽  
Coefficient Of Determination ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Climate Change Scenarios ◽  
Background Factors ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Environmental Background

Since groundwater is a major source of water for drinking and for industrial and irrigation uses, the identification of the environmental processes determining groundwater level fluctuation is potentially a matter of great consequence, especially in light of the fact that the frequency of extreme climate events may be expected to increase, causing changes in groundwater recharge systems. In the recent study, data measured at a frequency of one hour were collected from the Szigetköz, an inland delta of the Danube. These were then used to determine the presence, or not, and magnitude of any hidden environmental background factors that may be causing groundwater level fluctuations. Through the application of dynamic factor analysis, it was revealed that changes in groundwater level are mainly determined by (i) the water level of neighboring rivers and (ii) evapotranspiration. The intensity of these factors may also be estimated spatially. If the background factors determined by dynamic factor analysis do indeed figure in the linear model as variables, then the time series of groundwater levels can be said to have been accurately estimated with the use of linear regression. The accuracy of the estimate is indicated by the fact that adjusted coefficient of determination exceeds 0.9 in 80% of the wells. The results, via an enhanced understanding of the reasons for changes in the fluctuation of groundwater, could assist in the development of sustainable water management and irrigation strategies and the preparation for varying potential climate change scenarios.

scholarly journals Groundwater Modeling with Machine Learning Techniques: Ljubljana polje Aquifer

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 697 ◽  
Author(s):  
Klemen Kenda ◽  
Matej Čerin ◽  
Mark Bogataj ◽  
Matej Senožetnik ◽  
Kristina Klemen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Groundwater Modeling ◽  
Machine Learning Techniques ◽  
Physical Parameters ◽  
Strongly Correlated ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Decision Making Processes ◽  
Input Variables

In this study a thorough analysis is conducted concerning the prediction of groundwater levels of Ljubljana polje aquifer. Machine learning methodologies are implemented using strongly correlated physical parameters as input variables. The results show that data-driven modelling approaches can perform sufficiently well in predicting groundwater level changes. Different evaluation metrics confirm and highlight the capability of these models to catch the trend of groundwater level fluctuations. Despite the overall adequate performance, further investigation is needed towards improving their accuracy in order to be comprised in decision making processes.

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