Groundwater level prediction of Varanasi wells during pre-monsoon and post-monsoon using intelligence approach

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Shiwanshu Shekhar ◽  
Medha Jha
Keyword(s):  
Groundwater Level ◽  
Post Monsoon ◽  
Intelligence Approach ◽  
Groundwater Level Prediction

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 Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

2021 ◽  
Vol 15 (1) ◽  
pp. 1147-1158
Author(s):  
Shahab S. Band ◽  
Essam Heggy ◽  
Sayed M. Bateni ◽  
Hojat Karami ◽  
Mobina Rabiee ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Gaussian Process ◽  
Groundwater Level ◽  
Gaussian Process Regression ◽  
Arid Areas ◽  
Groundwater Level Prediction

Machine-learning-based regional-scale groundwater level prediction using GRACE

2021 ◽  
Vol 29 (3) ◽  
pp. 1027-1042 ◽  
Author(s):  
Pragnaditya Malakar ◽  
Abhijit Mukherjee ◽  
Soumendra N. Bhanja ◽  
Ranjan Kumar Ray ◽  
Sudeshna Sarkar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Regional Scale ◽  
Groundwater Level Prediction

scholarly journals Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

2020 ◽  
Vol 12 (21) ◽  
pp. 8932
Author(s):  
Kusum Pandey ◽  
Shiv Kumar ◽  
Anurag Malik ◽  
Alban Kuriqi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Uttar Pradesh ◽  
Groundwater Table ◽  
Post Monsoon ◽  
Groundwater Table Depth ◽  
Ann Models

Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GWR), groundwater discharge (GWD), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.

Groundwater Level Prediction Using Modified Linear Regression

2020 ◽  
Author(s):  
Madhuri Kommineni ◽  
K Veniha Reddy ◽  
K Jagathi ◽  
B Dushyanth Reddy ◽  
Roshini A ◽  
...  
Keyword(s):  
Linear Regression ◽  
Groundwater Level ◽  
Groundwater Level Prediction

scholarly journals An Integrated Use of GIS, Geostatistical and Map Overlay Techniques for Spatio-Temporal Variability Analysis of Groundwater Quality and Level in the Punjab Province of Pakistan, South Asia

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3555
Author(s):  
Huzaifa Shahzad ◽  
Hafiz Umar Farid ◽  
Zahid Mahmood Khan ◽  
Muhammad Naveed Anjum ◽  
Ijaz Ahmad ◽  
...  
Keyword(s):  
Groundwater Quality ◽  
Groundwater Level ◽  
Management Practices ◽  
Quality Parameters ◽  
Sodium Absorption ◽  
Distribution Analysis ◽  
East Part ◽  
Post Monsoon ◽  
Spatio Temporal ◽  
Lower South

The rapidly changing climatic scenario is demanding periodic evaluation of groundwater quality at the temporal and spatial scale in any region for its effectual management. The statistical, geographic information system (GIS), geostatistical, and map overlay approaches were applied for investigating the spatio-temporal variation in groundwater quality and level data of 242 monitoring wells in Punjab, Pakistan during pre-monsoon and post-monsoon seasons of the years 2015 and 2016. The analysis indicated the higher variation in data for both the seasons (pre-monsoon and post-monsoon) as coefficient of variation (CV) values were found in the range of 84–175% for groundwater quality parameters. Based on the t-test values, the marginal improvement in groundwater electrical conductivity (EC), sodium absorption ratio (SAR) and residual sodium carbonate (RSC) and decrease in groundwater level (GWL) were observed in 2016 as compared to 2015 (p = 0.05). The spatial distribution analysis of groundwater EC, SAR and RSC indicated that the groundwater quality was unfit for irrigation in the lower south-east part of the study area. The groundwater level (GWL) was also higher in that part of the study area during the pre-monsoon and post-monsoon seasons in 2015 and 2016. The overlay analysis also indicated that the groundwater EC, RSC and GWL values were higher in south-east parts of the study area during pre-monsoon and post-monsoon seasons of 2015 and 2016. Hence, there is an instant need to apply groundwater management practices in the rest of the region (especially in the lower south-east part) to overcome the future degradation of groundwater quality.

Application of set pair analysis model based on maximum similarity rules in groundwater level prediction

2018 ◽  
Vol 121 ◽  
pp. 244-249
Author(s):  
Xiu-Yu Zhang ◽  
Qi-Xiang Yang ◽  
Xuefang Du ◽  
Jing Tian
Keyword(s):  
Groundwater Level ◽  
Set Pair Analysis ◽  
Analysis Model ◽  
Model Based ◽  
Maximum Similarity ◽  
Pair Analysis ◽  
Groundwater Level Prediction
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