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Journal articles on the topic 'Groundwater level changing prediction'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Gonzalez, Rebeca Quintero, and Jamal Jokar Arsanjani. "Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 792. http://dx.doi.org/10.3390/ijgi10110792.

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Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the following research questions: (a) how will the water table be affected by climate change in the future based on different socio-economic pathways (SSPs

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Zheng, Yufeng, Dong Huang, Xiaoyi Fan, and Lili Shi. "Groundwater Level Prediction for Landslides Using an Improved TANK Model Based on Big Data." Water 16, no. 16 (2024): 2286. http://dx.doi.org/10.3390/w16162286.

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Geological conditions and rainfall intensity are two primary factors that can induce changes in groundwater level, which are one of the major triggering causes of geological disasters, such as collapse, landslides, and debris flow. In view of this, an improved TANK model is developed based on the influence of rainfall intensity, terrain, and geological conditions on the groundwater level in order to effectively predict the groundwater level evolution of rainfall landslides. A trapezoidal structure is used instead of the traditional rectangular structure to define the nonlinear change in a wate

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Wang, Hao, Quan Cai Wang, Xiao Ling Xu, and Qing Wu. "Analysis of Kinematic Behavior on Landslide Groundwater at K144 Section along Da-Yu Highway." Applied Mechanics and Materials 166-169 (May 2012): 1353–57. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1353.

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Most of landslides occurred because of the action of water. Dynamics of groundwater is directly related to the stability of landslide. Change of groundwater level is one of the most important factors of landslide prediction. In this paper, We used waterclocks to monitor groundwater level of K144 landslide for two years and found that different slope regions of the groundwater level showed different variation. Groundwater level of unstable region changes between 4m ~ 6m in the sandy mudstone area where average precipitations is about 1000 mm, which were the same trend with the monthly total rai

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Zhong, Shuang, Hui Geng, Fengjun Zhang, Zhaoying Liu, Tianye Wang, and Boyu Song. "Risk Assessment and Prediction of Heavy Metal Pollution in Groundwater and River Sediment: A Case Study of a Typical Agricultural Irrigation Area in Northeast China." International Journal of Analytical Chemistry 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/921539.

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The areas with typical municipal sewage discharge river and irrigation water function were selected as study sites in northeast China. The samples from groundwater and river sediment in this area were collected for the concentrations and forms of heavy metals (Cr(VI), Cd, As, and Pb) analysis. The risk assessment of heavy metal pollution was conducted based on single-factor pollution index (I) and Nemerow pollution index (NI). The results showed that only one groundwater sampling site reached a polluted level of heavy metals. There was a high potential ecological risk of Cd on the N21-2 sampli

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Kajewska-Szkudlarek, Joanna, Justyna Kubicz, and Ireneusz Kajewski. "Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits." Journal of Hydroinformatics 24, no. 1 (2021): 143–59. http://dx.doi.org/10.2166/hydro.2021.059.

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Abstract Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using sup

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Correia, Monica M., Thokozani Kanyerere, Nebo Jovanovic, Jacqueline Goldin, and Moyin John. "Climate and Groundwater Depth Relationships in Selected Breede Gouritz Water Management Area Subregions Between 2009 and 2020." Water 17, no. 13 (2025): 1969. https://doi.org/10.3390/w17131969.

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Groundwater resources are changing under the current climate change trajectory. Mitigation and adaptation measures include understanding the inter-working relationships among all climate variables and water resources, specifically groundwater, since it has less direct impacts than surface waters due to its nature. The Breede Gouritz Water Management Area provides an interesting platform to assess these interdependencies, since they have not been assessed before. To assess any underlying dependencies, a multivariate analysis of independent variables including monthly average temperature, summat

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Ghazavi, Reza, and Haidar Ebrahimi. "Predicting the impacts of climate change on groundwater recharge in an arid environment using modeling approach." International Journal of Climate Change Strategies and Management 11, no. 1 (2019): 88–99. http://dx.doi.org/10.1108/ijccsm-04-2017-0085.

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Purpose Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran. Design/methodology/approach A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels.

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Raphael, Oaikhena Oyanyan* Michael Akong Toko. "POTENTIAL IMPACT OF CLIMATE CHANGE ON GROUNDWATER RESOURCES IN PORT HARCOURT, NIGERIA." Global Journal of Engineering Science and Research Management 5, no. 2 (2018): 9–17. https://doi.org/10.5281/zenodo.1170640.

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Global warming is permanently changing the climate. Hydrological cycle is being intensified with increase in the rate of evaporation, condensation and precipitation resulting in frequent intense rainfall, flooding, sea level rise and drought in different parts of the world. Groundwater is connected to the hydrological cycle. The rate of aquifer’s recharge depends among others mainly on the amount of rainfall. Port Harcourt is part of the globe and not exempted from climate change; and groundwater is the main source of fresh water. Therefore, this study presents a critical evaluation of t

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Pulido-Velazquez, M., S. Peña-Haro, A. Garcia-Prats, et al. "Integrated assessment of the impact of climate and land use changes on groundwater quantity and quality in Mancha Oriental (Spain)." Hydrology and Earth System Sciences Discussions 11, no. 9 (2014): 10319–64. http://dx.doi.org/10.5194/hessd-11-10319-2014.

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Abstract. Climate and land use change (global change) impacts on groundwater systems cannot be studied in isolation, as various and complex interactions in the hydrological cycle take part. Land-use and land-cover (LULC) changes have a great impact on the water cycle and contaminant production and transport. Groundwater flow and storage are changing in response not only to climatic changes but also to human impacts on land uses and demands (global change). Changes in future climate and land uses will alter the hydrologic cycles and subsequently impact the quantity and quality of regional water

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10

Herath, Madhawa, Tharaka Jayathilaka, Hazi Mohammad Azamathulla, Vishwanadham Mandala, Namal Rathnayake, and Upaka Rathnayake. "Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka." Sensors 23, no. 7 (2023): 3680. http://dx.doi.org/10.3390/s23073680.

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Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteoro

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Safeeq, M., G. E. Grant, S. L. Lewis, M. G. Kramer, and B. Staab. "A geohydrologic framework for characterizing summer streamflow sensitivity to climate warming in the Pacific Northwest, USA." Hydrology and Earth System Sciences Discussions 11, no. 3 (2014): 3315–57. http://dx.doi.org/10.5194/hessd-11-3315-2014.

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Abstract. Summer streamflows in the Pacific Northwest are largely derived from melting snow and groundwater discharge. As the climate warms, diminishing snowpack and earlier snowmelt will cause reductions in summer streamflow. Most assessments of the impacts of a changing climate to streamflow make use of downscaled temperature and precipitation projections from General Circulation Models (GCMs). Projected climate simulations from these GCMs are often too coarse for planning purposes, as they do not capture smaller scale topographic controls and other important watershed processes. This uncert

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Tamagno, Santiago, Alison J. Eagle, Eileen L. McLellan, et al. "Predicting nitrate leaching loss in temperate rainfed cereal crops: relative importance of management and environmental drivers." Environmental Research Letters 17, no. 6 (2022): 064043. http://dx.doi.org/10.1088/1748-9326/ac70ee.

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Abstract Nitrate (NO3) leaching from agriculture represents the primary source of groundwater contamination and freshwater ecosystem degradation. At the field level, NO3 leaching is highly variable due to interactions among soil, weather and crop management factors, but the relative effects of these drivers have not been quantified on a global scale. Using a global database of 82 field studies in temperate rainfed cereal crops with 961 observations, our objectives were to (a) quantify the relative importance of environmental and management variables to identify key leverage points for NO3 miti

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Hansen, Annette K., Henrik Madsen, Peter Bauer-Gottwein, Anne Katrine V. Falk, and Dan Rosbjerg. "Multi-objective optimization of the management of a waterworks using an integrated well field model." Hydrology Research 43, no. 4 (2012): 430–44. http://dx.doi.org/10.2166/nh.2012.142.

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This study uses multi-objective optimization of an integrated well field model to improve the management of a waterworks. The well field model, called WELLNES (WELL field Numerical Engine Shell) is a dynamic coupling of a groundwater model, a pipe network model, and a well model. WELLNES is capable of predicting the water level and the energy consumption of the individual production wells. The model has been applied to Søndersø waterworks in Denmark, where it predicts the energy consumption within 1.8% of the observed. The objectives of the optimization problem are to minimize the specific ene

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14

Gyawali, Bimal, Dorina Murgulet, and Mohamed Ahmed. "Quantifying Changes in Groundwater Storage and Response to Hydroclimatic Extremes in a Coastal Aquifer Using Remote Sensing and Ground-Based Measurements: The Texas Gulf Coast Aquifer." Remote Sensing 14, no. 3 (2022): 612. http://dx.doi.org/10.3390/rs14030612.

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With the increasing vulnerability of groundwater resources, especially in coastal regions, there is a growing need to monitor changes in groundwater storage (GWS). Estimations of GWS have been conducted extensively at regional to global scales using GRACE and GRACE-FO observations. The major goal of this study was to evaluate the applicability of uninterrupted monthly GRACE-derived terrestrial water storage (TWSGRACE) records in facilitating detection of long- and short-term hydroclimatic events affecting the GWS in a coastal area. The TWSGRACE data gap was filled with reconstructed values fro

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15

Bhagat, Madhuri S., Aradhana Sahu, Ankush N. Asati, et al. "Precision Control Measures for Proactive Water Management to Improve Sustainability." WSEAS TRANSACTIONS ON SYSTEMS 24 (May 9, 2025): 367–76. https://doi.org/10.37394/23202.2025.24.32.

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Water resource management of sustainable development was an integral part of development, especially with regard to pollution, climatic fluctuation, and demands on water quality. This research will be aimed at prevention procedures, for the effective use of water, such as sophisticated mathematical models,monitoring, and the simulation systems. In this study, Linear Regression and Random Forest Regression models are used with the aim to estimate the various interactions between the pollutants, chemicals, thermal and groundwater, and water levels. Through the incorporation of real-time monitori

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Doglioni, Angelo, and Vincenzo Simeone. "Data-Driven Modelling of Water Table Oscillations for a Porous Aquifer Occasionally Flowing under Pressure." Geosciences 11, no. 7 (2021): 282. http://dx.doi.org/10.3390/geosciences11070282.

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Modelling of shallow porous aquifers in scenarios where boundary conditions change over time can be a difficult task. In particular, this is true when data modelling is pursued, i.e., models are directly constructed by measured data. In fact, data contain not only the information related to the physical phenomenon under investigation, but also the effects of time-varying boundary conditions, which work as a disturbance. This undesired component conditions the training of data-driven models, as they are fitted by models, which can produce predictions diverging from measured data. Here, a very s

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17

Zhao, Xianming, Zhimin Xu, and Yajun Sun. "Mechanism of Changes in Goaf Water Hydrogeochemistry: A Case Study of the Menkeqing Coal Mine." International Journal of Environmental Research and Public Health 20, no. 1 (2022): 536. http://dx.doi.org/10.3390/ijerph20010536.

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Goaf water in mining areas is widely found in China’s coal mines. To clarify the hydrogeochemical characteristics of goaf water and the influence mechanism of water–rock interaction and further reveal microbial action on the formation of goaf water quality, the goaf water in the Menkeqing coal mine was taken as the object, and physical modeling was used to simulate the process of the real goaf changing from an oxygen-sufficient environment to an anoxic environment with the rise of groundwater level in this work. The experimental results showed that the water–rock interaction in the goaf was ma

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18

Zhang, Hui, Shengnan Zhao, Xiaohong Shi, Jinda Zhang, Zhimou Cui, and Jingyi Wang. "The Influence of Freeze–Thaw Process on the Dynamic Changes in Body Weight and Metal in Groundwater of Seasonal Frozen Lakes: Experimental Study and Model Simulation." Toxics 13, no. 4 (2025): 288. https://doi.org/10.3390/toxics13040288.

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To investigate the changes in heavy metal content in the sub glacial water during the freezing and thawing process of seasonally frozen lakes, the Wuliangsuhai Lake in northern China was taken as the research object. The ice thickness, water depth, and heavy metal content at different depths of the lake were measured during the freezing and thawing periods. Based on a large amount of measured lake heavy metal data, MATLAB 2022b software is used to model data fitting and optimization identification, and wavelet analysis and 24 h sliding average method are used for verification analysis to descr

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Zhang, Shulei, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai. "Representation of a two-way coupled irrigation system in the Common Land Model." Hydrology and Earth System Sciences 29, no. 14 (2025): 3119–43. https://doi.org/10.5194/hess-29-3119-2025.

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Abstract. Human land–water management, especially irrigation water withdrawal and use, significantly impacts the global and regional water cycle, energy budget, and near-surface climate. While land surface models are widely used to explore and predict the impacts of irrigation, the irrigation system representation in these models is still in its early stages. This study enhances the Common Land Model (CoLM) by introducing a two-way coupled irrigation module. This module includes an irrigation water demand scheme based on soil moisture deficit, an irrigation application scheme considering four

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Zhao, Wei Guo, and Li Ying Wang. "Fuzzy Neural Network for Groundwater Level Prediction." Applied Mechanics and Materials 29-32 (August 2010): 2794–98. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2794.

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In this paper, prediction system is developed based on a fuzzy neural network(FNN) by using the past groundwater level data to discover fuzzy rules and make future predictions. The learning algorithm is implemented to the past historical data. Compared to other predictors, our results show that the FNN predictor can reduce significantly both relative mean errors and root mean squared errors of predicted groundwater level. It is demonstrated that FNN performs well for groundwater data analysis and its feasibility of applying FNN to groundwater level prediction.

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Sierikova, Olena, Volodymyr Koloskov, and Elena Strelnikova. "The groundwater level changing processes modeling in 2D and 3D formulation." Acta Periodica Technologica, no. 53 (2022): 36–47. http://dx.doi.org/10.2298/apt2253036s.

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The objective of this study was to develop a mathematical model to determine the tendency of the groundwater level changes under the influence of external factors to prevent environmentally hazardous impacts and emergency situations. Mathematical methods (analytical solution of differential filtration equations involved the computer program Maple) - for creation the groundwater level changes model, methods of ecological and economic assessment and comparative analysis - for the identification of groundwater level impact important factors and groundwater level impact on the environment, balance

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Podgorski, Joel, and Michael Berg. "Global threat of arsenic in groundwater." Science 368, no. 6493 (2020): 845–50. http://dx.doi.org/10.1126/science.aba1510.

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Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 mill

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Dash, Nikunja B., Sudhindra N. Panda, Renji Remesan, and Narayan Sahoo. "Hybrid neural modeling for groundwater level prediction." Neural Computing and Applications 19, no. 8 (2010): 1251–63. http://dx.doi.org/10.1007/s00521-010-0360-1.

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Nalarajan, Nitha Ayinippully, and C. Mohandas. "Groundwater Level Prediction using M5 Model Trees." Journal of The Institution of Engineers (India): Series A 96, no. 1 (2015): 57–62. http://dx.doi.org/10.1007/s40030-014-0093-8.

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Chen, Hsin-Yu, Zoran Vojinovic, Weicheng Lo, and Jhe-Wei Lee. "Groundwater Level Prediction with Deep Learning Methods." Water 15, no. 17 (2023): 3118. http://dx.doi.org/10.3390/w15173118.

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The development of civilization and the preservation of environmental ecosystems are strongly dependent on water resources. Typically, an insufficient supply of surface water resources for domestic, industrial, and agricultural needs is supplemented with groundwater resources. However, groundwater is a natural resource that must accumulate over many years and cannot be recovered after a short period of recharge. Therefore, the long-term management of groundwater resources is an important issue for sustainable development. The accurate prediction of groundwater levels is the first step in evalu

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Zhao, Wei Guo, Huan Wang, and Zi Jun Wang. "Groundwater Level Forecasting Based on Support Vector Machine." Applied Mechanics and Materials 44-47 (December 2010): 1365–69. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1365.

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In this paper, we apply support vector regression (SVR) for groundwater level forecasting and compare its results to other prediction methods using real groundwater level data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector regression will perform well for time series analysis. Compared to other predictors, our results show that the SVR predictor can reduce significantly both relative mean errors and root mean squared errors of predicted groundwater level. We demonstrate the feasibility o

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Bozorg-Haddad, Omid, Mohammad Delpasand, and Hugo A. Loáiciga. "Self-optimizer data-mining method for aquifer level prediction." Water Supply 20, no. 2 (2019): 724–36. http://dx.doi.org/10.2166/ws.2019.204.

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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

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Affandi, Azhar K., Kunio Watanabe, and Haryadi Tirtomihardjo. "Use of Back-propagation Artificial Neural Networks for Groundwater Level Simulation." Asian Journal of Water, Environment and Pollution 5, no. 1 (2008): 57–65. http://dx.doi.org/10.3233/ajw-2008-5_1_10.

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This article presents simulation of groundwater level fluctuation based on an artificial neural network modelling. The prediction used multi-layer back-propagation neural networks (BPANN). The case of study area was Jakarta, Indonesia, that has high population density and several purposes of groundwater resource usage. Input variables were using delay five-daily groundwater level fluctuation (GLF) of observation well interest to predict current GLF. The applicability of BPANN for GLF prediction was verified in three sets of input variables. The result showed that application of BPANN to simula

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Shin, Mun-Ju, Soo-Hyoung Moon, Kyung Goo Kang, Duk-Chul Moon, and Hyuk-Joon Koh. "Analysis of Groundwater Level Variations Caused by the Changes in Groundwater Withdrawals Using Long Short-Term Memory Network." Hydrology 7, no. 3 (2020): 64. http://dx.doi.org/10.3390/hydrology7030064.

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To properly manage the groundwater resources, it is necessary to analyze the impact of groundwater withdrawal on the groundwater level. In this study, a Long Short-Term Memory (LSTM) network was used to evaluate the groundwater level prediction performance and analyze the impact of the change in the amount of groundwater withdrawal from the pumping wells on the change in the groundwater level in the nearby monitoring wells located in Jeju Island, Korea. The Nash–Sutcliffe efficiency between the observed and simulated groundwater level was over 0.97. Therefore, the groundwater prediction perfor

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Yadav, Basant, Sudheer Ch, Shashi Mathur, and Jan Adamowski. "Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction." Journal of Water and Land Development 32, no. 1 (2017): 103–12. http://dx.doi.org/10.1515/jwld-2017-0012.

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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

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Cao, Ying, Kunlong Yin, Chao Zhou, and Bayes Ahmed. "Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis." Sensors 20, no. 3 (2020): 845. http://dx.doi.org/10.3390/s20030845.

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The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater l

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Porte, Pallavi, Rajendra Kumar Isaac, Kipoo Kiran Singh Mahilang, Khilendra Sonboier, and Pankaj Minj. "Groundwater Level Prediction Using Artificial Neural Network Model." International Journal of Current Microbiology and Applied Sciences 7, no. 2 (2018): 2947–54. http://dx.doi.org/10.20546/ijcmas.2018.702.358.

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Chen, Lu-Hsien, Ching-Tien Chen, and Yan-Gu Pan. "Groundwater Level Prediction Using SOM-RBFN Multisite Model." Journal of Hydrologic Engineering 15, no. 8 (2010): 624–31. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000218.

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Adamowski, Kaz, and W. Feluch. "Application of nonparametric regression to groundwater level prediction." Canadian Journal of Civil Engineering 18, no. 4 (1991): 600–606. http://dx.doi.org/10.1139/l91-073.

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A new nonparametric regression model is proposed to investigate the relationship between groundwater level fluctuations and streamflow time series observations. The developed nonparametric model does not force the relationship between variables into a rigidly defined class (i.e., linear regression) and is capable of inferring complicated relationships. The results from the analysis indicate that the nonparametric method gives more accurate prediction results than those obtained from parametric regression. A split-sample experiment shows that nonparametric regression gives accurate prediction (

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Intui, Sutasinee, and Shinya Inazumi. "Experimental and Analytical Evaluations of Ground Behaviors on Changing in Groundwater Level in Bangkok, Thailand." Water 15, no. 10 (2023): 1825. http://dx.doi.org/10.3390/w15101825.

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Groundwater level changes have numerous effects on buildings, such as differential ground deformation and cracking on the wall. In Bangkok, Thailand, change in groundwater levels changing was caused by groundwater pumping that took place from 1978 to 1997. This is the main effect of ground deformation in a wide area of the Bangkok plain. According to the regulation of groundwater pumping in Bangkok and urban areas, the trend of groundwater level tended to recover around the year 1997. However, the ground settlement still occurs for a while after groundwater recovery. The objective of this stud

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Kombo, Omar Haji, Santhi Kumaran, Yahya H. Sheikh, Alastair Bovim, and Kayalvizhi Jayavel. "Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique." Hydrology 7, no. 3 (2020): 59. http://dx.doi.org/10.3390/hydrology7030059.

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Reliable seasonal prediction of groundwater levels is not always possible when the quality and the amount of available on-site groundwater data are limited. In the present work, a hybrid K-Nearest Neighbor-Random Forest (KNN-RF) is used for the prediction of variations in groundwater levels (L) of an aquifer with the groundwater relatively close to the surface (<10 m) is proposed. First, the time-series smoothing methods are applied to improve the quality of groundwater data. Then, the ensemble K-Nearest Neighbor-Random Forest (KNN-RF) model is treated using hydro-climatic data for the pred

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S, Ranganathan, Ranjith Kumar K, and Vignesh M. "AI-Driven Groundwater Level Enhancement System using Advanced Prediction Algorithms." March 2024 6, no. 1 (2024): 55–69. http://dx.doi.org/10.36548/jscp.2024.1.005.

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This research focuses on predicting water sources in various areas by analyzing historical data on groundwater levels, rainfall, and borewells. The study explores the relationships between groundwater levels and environmental factors, emphasizing the influence of rainfall on aquifer recharge. Borewell data, including depth and water quality, is incorporated to identify potential water sources. The research involves data cleaning, exploratory analysis, and machine learning to predict groundwater levels based on diverse features such as rainfall patterns and geographical characteristics. Spatial

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Bayat, Mandana, Saeid Eslamian, Gholamreza Shams, and Alborz Hajiannia. "Groundwater Level Prediction through GMS Software – Case Study of Karvan Area, Iran." Quaestiones Geographicae 39, no. 3 (2020): 139–45. http://dx.doi.org/10.2478/quageo-2020-0028.

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AbstractIran, being located in arid and semi-arid regions, faces an increase in human demand for water, and the global climate change has led to the excessive use of groundwater. China, India and Iran were ranked from first to third, respectively, in excessive groundwater consumption in 2005. The effects of effective parameters on groundwater recharge such as precipitation, surface recharge and well water harvesting in the Karvan aquifer are assessed. Groundwater flow models have typically been and are being adopted since the beginning of this millennium to better manage groundwater resources.

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Mohanasundaram, S., G. Suresh Kumar, and Balaji Narasimhan. "A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions." H2Open Journal 2, no. 1 (2019): 25–44. http://dx.doi.org/10.2166/h2oj.2019.022.

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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

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Manna, Tishya, and Anitha A. "Deep Ensemble-Based Approach Using Randomized Low-Rank Approximation for Sustainable Groundwater Level Prediction." Applied Sciences 13, no. 5 (2023): 3210. http://dx.doi.org/10.3390/app13053210.

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Groundwater is the most abundant freshwater resource. Agriculture, industrialization, and domestic water supplies rely on it. The depletion of groundwater leads to drought mitigation. Topographic elevation, aquifer properties, and geomorphology influence groundwater quality. As the groundwater level data (GWL) are time series in nature, it is challenging to determine appropriate metrics and to evaluate groundwater levels accurately with less information loss. An effort has been made to forecast groundwater levels in India by developing a deep ensemble learning approach using a double-edge bi-d

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Jalalkamali, Amir, Hossein Sedghi, and Mohammad Manshouri. "Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran." Journal of Hydroinformatics 13, no. 4 (2010): 867–76. http://dx.doi.org/10.2166/hydro.2010.034.

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The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neuro-fuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells. Also based on compar

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Wang, Li Ying, and Wei Guo Zhao. "Forecasting Groundwater Level Based on Relevance Vector Machine." Advanced Materials Research 121-122 (June 2010): 43–47. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.43.

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Relevance Vector Machine (RVM) is a novel kernel method based on sparse Bayesian, which has many advantages such as its kernel functions without the restriction of Mercer’s conditions, and the relevance vectors are automatically determined and have fewer parameters. In this paper, the RVM model is applied to forecasting groundwater level. The experimental results show the final RVM model achieved is sparser, the prediction precision is higher and the prediction values are in better agreement with the real values. It can be concluded that this technique can be seen as a very promising option to

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Reynolds, W. D., R. de Jong, I. J. van Wesenbeeck, and R. S. Clemente. "Prediction of Pesticide Leaching on a Watershed Basis: Methodology and Application." Water Quality Research Journal 30, no. 3 (1995): 365–81. http://dx.doi.org/10.2166/wqrj.1995.033.

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Abstract Methodology consisting of a numerical solute transport model (LEACHM), combined with geostatistical analysis (kriging) and a geographic information system (GIS), was developed to predict and characterize low-level, nonpoint source pesticide contamination of groundwater on a watershed basis. A preliminary test and evaluation of the methodology was conducted by applying it to the prediction of atrazine contamination of groundwater in the Grand River watershed in Southern Ontario. Atrazine loading to the groundwater was predicted to be highly variable spatially (CV = 164%), but low-level

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Chu, Haibo, Jianmin Bian, Qi Lang, Xiaoqing Sun, and Zhuoqi Wang. "Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information." Sustainability 14, no. 18 (2022): 11598. http://dx.doi.org/10.3390/su141811598.

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Daily groundwater level is an indicator of groundwater resources. Accurate and reliable groundwater level (GWL) prediction is crucial for groundwater resources management and land subsidence risk assessment. In this study, a representative deep learning model, long short-term memory (LSTM), is adopted to predict groundwater level with the selected predictors by partial mutual information (PMI), and bootstrap is employed to generate different samples combination for training many LSTM models, and the predicted values by many LSTM models are used for the uncertainty assessment of groundwater lev

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Li, Ying, Gui Zhang Zhao, Lei Zeng, and Cun Liang Wang. "Vegetation-Groundwater Relationship Model for Subei Lake Watershed and Prediction of Vegetation Succession Rules under Exploitation." Advanced Materials Research 518-523 (May 2012): 4315–20. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4315.

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Located in an arid and semi-arid region with low rainfall and high evaporation, the Subei Lake watershed has fragile ecological environment; the impact of groundwater level recession on vegetation ecology is the key problem for the exploitation and utilization of groundwater resource in this region. In this paper, a succession model for vegetation and burial depth of groundwater level was constructed through field survey, and was used along with numeric simulation to predict the change in burial depth of groundwater level in the study area under exploitation and to analyze and predict the vege

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Khedri, Akbar, Nasrollah Kalantari, and Meysam Vadiati. "Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer." Water Supply 20, no. 3 (2020): 909–21. http://dx.doi.org/10.2166/ws.2020.015.

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Abstract Accurate and reliable groundwater level prediction is an important issue in groundwater resource management. The objective of this research is to compare groundwater level prediction of several data-driven models for different prediction periods. Five different data-driven methods are compared to evaluate their performances to predict groundwater levels with 1-, 2- and 3-month lead times. The four quantitative standard statistical performance evaluation measures showed that while all models could provide acceptable predictions of groundwater level, the least square support vector mach

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Kajewska-Szkudlarek, Joanna, and Wojciech Łyczko. "Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting." Water 13, no. 6 (2021): 778. http://dx.doi.org/10.3390/w13060778.

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Effective groundwater planning and management should be based on the prediction of available water volume. The complex nature of groundwater systems makes this complicated and requires the use of complex methods. Data-driven models using computational intelligence are becoming increasingly popular in that field. The key issue in predictive modelling is the selection of input variables. Wrocław-Osobowice irrigation fields were a wastewater treatment plant until 2013. The monitoring of groundwater levels is being continued to assess the water relations in that area after the end of their exploit

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Dong, Thanh Uyen, Chung Van Le, and Nang Van Nguyen. "Determine the exploitable groundwater reserve of Con Son Island in the condition of climate change and sea level rising." Science and Technology Development Journal 20, K4 (2017): 13–20. http://dx.doi.org/10.32508/stdj.v20ik4.1108.

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The paper deals with problem of groundwater sustainable exploitation in accordance with the allowable drawdown, stipulated in Circular 27/2014/TT-BTNMT of Ministry of Natural Resources and Environment in combination with the sustainablity index, advised by UNESCO. The technical method is using a groundwater model containing reliable input data. As a result, a groundwater flow model was successfully constructed for stimulating the actual groundwater system in Con Son Island. The model was calibrated, using groundwater monitoring data of 12 wells and produced an error less than ±0.5m. On the bas

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Sumiya, Nazmoon Nahar, and Hafiza Khatun. "Groundwater Variability In Bangladesh: Assessment Based On Rainfall Variation And Use Of Water In Irrigation." Journal of the Asiatic Society of Bangladesh, Science 42, no. 2 (2016): 177–89. http://dx.doi.org/10.3329/jasbs.v42i2.46221.

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This study attempts to portray the scenario of groundwater level with respect to rainfall variability and its use for irrigation purpose for rice production in Bangladesh. Data on groundwater level and irrigation water usage were collected from BWDB and BBS. The changing pattern of groundwater level are presented in maps using Inverse Distance Weighted (IDW) interpolation method in ArcGIS 10.3. Analysis shows the increasing dependency on groundwater than on surface water for irrigation purpose at varied range across the country. The groundwater level is declining at a higher rate in northern p

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Zhang, Hui, Jixuan Zhao, and Chong Chen. "Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China." E3S Web of Conferences 266 (2021): 09005. http://dx.doi.org/10.1051/e3sconf/202126609005.

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Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult.. Therefore, this paper proposesthe BP (Back Propagation) neural network and the Radial Basis Function (RBF) neural network. The study area is divided into two zones. The R2 (coefficient of determination) and RMSE (Root Mean Squared Error) are used to evaluate the performance. The BP neural network is used to predict groundwater level in the two zones with the R2of0.57 and 0.54, with the RMSE of 0.0804 meters and 0.1864 meters respective

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