scholarly journals Modelling groundwater level fluctuation in an Indian coastal aquifer

Water SA ◽  
2020 ◽  
Vol 46 (4 October) ◽  
Author(s):  
Safieh Javadinejad ◽  
Rebwar Dara ◽  
Forough Jafary
Keyword(s):  
Groundwater Level ◽  
Coastal Aquifer ◽  
M5 Model Tree ◽  
Groundwater Levels ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Input Variables ◽  
Lag Times ◽  
Observation Wells

Estimating groundwater level (GWL) fluctuations is a vital requirement in hydrology and hydraulic engineering, and is commonly addressed through artificial intelligence (AI) models. The purpose of this research was to estimate groundwater levels using new modelling methods. The implementation of two separate soft computing techniques, a multilayer perceptron neural network (MLPNN) and an M5 model tree (M5-MT), was examined. The models are used in the estimation of monthly GWLs observed in a shallow unconfined coastal aquifer. Data for the water level were collected from observation wells located near Ganjimatta, India, and used to estimate GWL fluctuation. To do this, two scenarios were provided to achieve optimal input variables for modelling the GWL at the present time. The input parameters applied for developing the proposed models were a monthly time-series of summed rainfall, the mean temperature (within its lag times that have an effect on groundwater), and historical GWL observations throughout the period 1996–2006. The efficiency of each proposed model for Ganjimatt was investigated in stages of trial and error. A performance evaluation showed that the M5-MT outperformed the MLPNN model in estimating the GWL in the aquifer case study. Based on the M5-MT approach, the development of this model gives acceptable results for the Indian coastal aquifers. It is recommended that water managers and decision makers apply these new methods to monitor groundwater conditions and inform future planning.

scholarly journals Investigating Groundwater Condition and Seawater Intrusion Status in Coastal Aquifer Systems of Eastern India

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1952
Author(s):  
Subrata Halder ◽  
Lingaraj Dhal ◽  
Madan K. Jha
Keyword(s):  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Groundwater Level ◽  
Coastal Aquifer ◽  
Quality Data ◽  
Confined Aquifer ◽  
Groundwater Levels ◽  
Post Monsoon ◽  
Aquifer Systems ◽  
Sustainable Water Supply

Providing sustainable water supply for domestic needs and irrigated agriculture is one of the most significant challenges for the current century. This challenge is more daunting in coastal regions. Groundwater plays a pivotal role in addressing this challenge and hence, it is under growing stress in several parts of the world. To address this challenge, a proper understanding of groundwater characteristics in an area is essential. In this study, spatio-temporal analyses of pre-monsoon and post-monsoon groundwater-levels of two coastal aquifer systems (upper leaky confined and underlying confined) were carried out in Purba Medinipur District, West Bengal, India. Trend analysis of seasonal groundwater-levels of the two aquifers systems was also performed using Mann-Kendall test, Linear Regression test, and Innovative Trend test. Finally, the status of seawater intrusion in the two aquifers was evaluated using available groundwater-quality data of Chloride (Cl−) and Total Dissolve Solids (TDS). Considerable spatial and temporal variability was found in the seasonal groundwater-levels of the two aquifers. Further, decreasing trends were spotted in the pre-monsoon and post-monsoon groundwater-level time series of the leaky confined and confined aquifers, except pre-monsoon groundwater-levels in Contai-I and Deshpran blocks, and the post-monsoon groundwater-level in Ramnagar-I block for the leaky confined aquifer. The leaky confined aquifer in Contai-I, Contai-III, and Deshpran blocks and the confined aquifer in Nandigram-I and Nandigram-II blocks are vulnerable to seawater intrusion. There is an urgent need for the real-time monitoring of groundwater-levels and groundwater quality in both the aquifer systems, which can ensure efficient management of coastal groundwater reserves.

scholarly journals A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions

2019 ◽  
Vol 2 (1) ◽  
pp. 25-44 ◽  
Author(s):  
S. Mohanasundaram ◽  
G. Suresh Kumar ◽  
Balaji Narasimhan
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Moving Average ◽  
Seasonal Component ◽  
Univariate Time Series ◽  
Level Data ◽  
Proposed Model ◽  
Interval Approach ◽  
Prediction And Forecasting ◽  
Observation Wells

Abstract Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model's performance with R2 values of 0.82 and 0.93 for the corresponding wells' groundwater level data.

scholarly journals Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

2017 ◽  
Vol 32 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Basant Yadav ◽  
Sudheer Ch ◽  
Shashi Mathur ◽  
Jan Adamowski
Keyword(s):  
Soft Computing ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Support Vector ◽  
Complex Nature ◽  
Data Set ◽  
Groundwater Levels ◽  
Soft Computing Techniques ◽  
Learning Machine ◽  
The Comparative Study

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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.

scholarly journals Wildfire Impacts on Groundwater Aquifers: A Case Study of the 1996 Honey Boy Fire in Beaver County, Utah, USA

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2279
Author(s):  
Benjamin T. Johnk ◽  
David C. Mays
Keyword(s):  
Groundwater Level ◽  
Temporary Reduction ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Monitoring Wells ◽  
Groundwater Aquifers ◽  
Before And After ◽  
Beaver County ◽  
Level Reduction

It is well known that wildfires destroy vegetation and form soil crusts, both of which increase stormwater runoff that accelerates erosion, but less attention has been given to wildfire impacts on groundwater aquifers. Here, we present a systematic study across the contiguous United States to test the hypothesis that wildfires reduce infiltration, indicated by temporary reductions in groundwater levels. Geographic information systems (GIS) analysis performed using structured queried language (SQL) categorized wildfires by their proximity to wells with publicly available monitoring data. Although numerous wildfires were identified with nearby monitoring wells, most of these data were confounded by unknown processes, preventing a clear acceptance or rejection of the hypothesis. However, this analysis did identify a particular case study, the 1996 Honey Boy Fire in Beaver County, Utah, USA that supports the hypothesis. At this site, daily groundwater data from a well located 790 m from the centroid of the wildfire were used to assess the groundwater level before and after the wildfire. A sinusoidal time series adjusted for annual precipitation matches groundwater level fluctuations before the wildfire but cannot explain the approximately two-year groundwater level reduction after the wildfire. Thus, for this case study, there is a correlation, which may be causal, between the wildfire and temporary reduction in groundwater levels. Generalizing this result will require further research.

scholarly journals Self-optimizer data-mining method for aquifer level prediction

2019 ◽  
Vol 20 (2) ◽  
pp. 724-736
Author(s):  
Omid Bozorg-Haddad ◽  
Mohammad Delpasand ◽  
Hugo A. Loáiciga
Keyword(s):  
Groundwater Level ◽  
Hybrid Algorithm ◽  
Water Source ◽  
Support Vector ◽  
Mining Method ◽  
Parameter Calibration ◽  
Groundwater Levels ◽  
Municipal Water Supply ◽  
Predictive Skill ◽  
Observation Wells

Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply in Iran. The GA–SVR's groundwater-level predictions were compared to those from genetic programming (GP). Results show that the randomized approach of GA–SVR prediction yields R2 values ranging between 0.88 and 0.995, and root mean square error (RMSE) values ranging between 0.13 and 0.258 m, which indicates better groundwater-level predictive skill of GA-SVR compared to GP, whose R2 and RMSE values range between 0.48–0.91 and 0.15–0.44 m, respectively.

scholarly journals Comparing Approaches for Reconstructing Groundwater Levels in the Mountainous Regions of Interior British Columbia, Canada, Using Tree Ring Widths

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1374
Author(s):  
Stephanie C. Hunter ◽  
Diana M. Allen ◽  
Karen E. Kohfeld
Keyword(s):  
British Columbia ◽  
Tree Ring ◽  
Groundwater Level ◽  
High Elevation ◽  
Valuable Insight ◽  
Driven Systems ◽  
Groundwater Levels ◽  
Mountainous Regions ◽  
Tree Ring Data ◽  
Observation Wells

Observed groundwater level records are relatively short (<100 years), limiting long-term studies of groundwater variability that could provide valuable insight into climate change effects. This study uses tree ring data from the International Tree Ring Database (ITRDB) and groundwater level data from 22 provincial observation wells to evaluate different approaches for reconstructing groundwater levels from tree ring widths in the mountainous southern interior of British Columbia, Canada. The twenty-eight reconstruction models consider the selection of observation wells (e.g., regional average groundwater level vs. wells classified by recharge mechanism) and the search area for potential tree ring records (climate footprint vs. North American Ecoregions). Results show that if the climate footprint is used, reconstructions are statistically valid if the wells are grouped according to recharge mechanism, with streamflow-driven and high-elevation recharge-driven wells (both snowmelt-dominated) producing valid models. Of all the ecoregions considered, only the Coast Mountain Ecoregion models are statistically valid for both the regional average groundwater level and high-elevation recharge-driven systems. No model is statistically valid for low-elevation recharge-driven systems (rainfall-dominated). The longest models extend the groundwater level record to the year 1500, with the highest confidence in the later portions of the reconstructions going back to the year 1800.

scholarly journals Relationship between rainfall and water table in a coastal aquifer: the case study of Castelporziano presidential estate

2019 ◽  
Author(s):  
Francesca Banzato ◽  
Marino Domenico Barberio ◽  
Andrea Del Bon ◽  
Alessandro Lacchini ◽  
Valentina Marinelli ◽  
...  
Keyword(s):  
Coastal Area ◽  
Coastal Aquifer ◽  
Monitoring Network ◽  
Seasonal Effect ◽  
Volcanic Aquifer ◽  
Steady Regime ◽  
Flow Paths ◽  
Groundwater Levels ◽  
Northern Sector

This study is focused on the analysis of seasonal and annual variability in groundwater levels of the coastal aquifer of Castelporziano Presidential Estate, a protected area of 59 Km2 located in the periphery of Rome. A comparison with the local trends of rainfall at “Castello” gauging station at different time scales (monthly, seasonal and annual) has been carried out. The results highlight differences between the coastal area and eastern and northern sector of the Estate. Indeed, the seasonal effect due to local meteoric recharge is direct and regular during the year in the coastal area in respect to the eastern and northern sectors of the Estate. Moreover, annual steady regime and multi-year trend of groundwater levels suggest the contribution from the adjacent volcanic aquifer of Albani Hills. In the latter case, the regional circulation of groundwater is affected by the effects of intense withdrawals. The maintenance of the monitoring network will allow to define the flow paths of the groundwater that characterize the coastal aquifer of Castelporziano.

scholarly journals The Value of Hydrologic Information in Reservoir Outflow Decision-Making

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1372 ◽  
Author(s):  
Kebing Chen ◽  
Shenglian Guo ◽  
Shaokun He ◽  
Tao Xu ◽  
Yixuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Simulation Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Flood Season ◽  
Proposed Model ◽  
Importance Ranking ◽  
Input Variables ◽  
The One

The controlled outflows from a reservoir are highly dependent on the decisions made by the reservoir operators who mainly rely on available hydrologic information, such as past outflows, reservoir water level and forecasted inflows. In this study, Random Forests (RF) algorithm is used to build reservoir outflow simulation model to evaluate the value of hydrologic information. The Three Gorges Reservoir (TGR) in China is selected as a case study. As input variables of the model, the classic hydrologic information is divided into past, current and future information. Several different simulation models are established based on the combinations of these three groups of information. The influences and value of hydrologic information on reservoir outflow decision-making are evaluated from two different perspectives, the one is the simulation result of different models and the other is the importance ranking of the input variables in RF algorithm. Simulation results demonstrate that the proposed model is able to reasonably simulate outflow decisions of TGR. It is shown that past outflow is the most important information and the forecasted inflows are more important in the flood season than in the non-flood season for reservoir operation decision-making.

scholarly journals Characteristic groundwater level regimes in the capture zones of radial collector wells and importance of identification (Case study of Belgrade Groundwater Source)

2016 ◽  
pp. 43-53 ◽  
Author(s):  
Djordjije Bozovic ◽  
Dusan Polomcic ◽  
Dragoljub Bajic
Keyword(s):  
Groundwater Level ◽  
Background Information ◽  
Preliminary Assessment ◽  
Groundwater Levels ◽  
Operating Modes ◽  
Capture Zones ◽  
Level Regime ◽  
Groundwater Source ◽  
Discharge Capacities

Assessment of the operating modes of radial collector wells reveals that the pumping levels in the well caissons are very low relative to the depth/elevation of the laterals, which is a common occurrence at Belgrade Groundwater Source. As a result, well discharge capacities vary over a broad range and groundwater levels in the capture zones differ even when the rate of discharge is the same. Five characteristic groundwater level regimes are identified and their origin is analyzed using representative wells as examples. The scope and type of background information needed to identify the groundwater level regime are presented and an interpretation approach is proposed for preliminary assessment of the aquifer potential at the well site for providing the needed amount of groundwater.

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