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Comparison of numerical model, neural intelligent and GeoStatistical in estimating groundwater table
Dr Maryam Bayatvarkeshi, Ms Rojin Fasihi,
Volume 18, Issue 48 (3-2018)
Abstract
Modeling provides the studying of groundwater managers as an efficient method with the lowest cost. The purpose of this study was comparison of the numerical model, neural intelligent and geostatistical in groundwater table changes modeling. The information of Hamedan – Bahar aquifer was studied as one of the most important water sources in Hamedan province. In this study, MODFLOW numerical code in GMS software, artificial neural network (ANN) and neural – fuzzy (CANFIS) method in NeuroSolution software, wavelet-neural method in MATLAB software and geostatistical method in ArcGIS software were used. The results showed that the accuracy of methods in estimation of the groundwater table with the lowest Normal Root Mean Square Error (NRMSE) include Wavelet-ANN, CANFIS, geostatistical, ANN and numerical model, respectively. The NRMSE value in Wavelet-ANN method as optimization method was 0.11 % and in numerical model was 2.2 %. Also the correlation coefficients were 0.998 and 0.904, respectively. So application of neural combination models, specially, wavelet theory in estimated the groundwater table is most suitable than geostatistical and numerical model. Moreover, in the neural intelligent models were applied latitude, longitude and altitude as available variables in input models. The zoning results of groundwater table indicated that the decreased trend of groundwater table was from the west to the east of aquifer which was in line with the hydraulic gradient.
 

Investigation of land use changes and its relationship with groundwater level Case study: Meshginshahr plain
Mahrookh Ghazayi, Nazfar Aghazadeh, Ehsan Ghaleh, Elhameh Ebaddyy,
Volume 25, Issue 79 (12-2025)
Abstract
The depletion of surface water resources has necessitated uncontrolled groundwater abstraction in various regions worldwide, resulting in substantial reductions in groundwater table levels. As populations continue to expand, the extraction of these essential resources has intensified, posing a significant threat to natural reserves. This study aims to monitor groundwater levels through the analysis of satellite imagery and to investigate the correlation between these levels and land use patterns. To accomplish this objective, relevant satellite images were acquired and subjected to appropriate pre-processing. An object-oriented methodology was employed to generate land use classification maps for two distinct years, alongside a land use change map covering a fifteen-year period from 2000 to 2015. Moreover, groundwater level maps for the study area were produced for both years utilizing the Gaussian method, recognized as the most accurate approach. The findings indicate a robust and significant relationship between land use and groundwater levels, revealing that areas with higher vegetation exhibit lower groundwater levels compared to other regions. This phenomenon can be attributed to the hydrological dynamics that facilitate the movement of water from higher potential zones to these areas. Additionally, irrigated agricultural practices demonstrated the most pronounced average decline in water levels relative to other land uses, underscoring the excessive reliance on groundwater for irrigation in the study area. The results further illustrate that the conventional kriging method with Gaussian variance surpasses other techniques in estimating groundwater table depths across both statistical periods. Analysis through conventional kriging reveals a general decline in groundwater levels throughout the majority of the plain during the study period, with a maximum decrease of 40 meters and an average reduction of 15 meters.

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This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)