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Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique

https://doi.org/10.3390/hydrology7030059
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Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review
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Groundwater level predictions in the Thames Basin, London over extended horizons using Transformers and advanced machine learning models
inferred← citeshttps://doi.org/10.1016/j.jobe.2025.111938openalex-snapshot
Dynamic personalized thermal comfort Model:Integrating temporal dynamics and environmental variability with individual preferences
inferred← citeshttps://doi.org/10.1016/j.microc.2025.112815openalex-snapshot
Modeling polyethylene glycol density using robust soft computing methods
inferred← citeshttps://doi.org/10.1016/j.neucom.2022.03.014openalex-snapshot
Groundwater level prediction using machine learning models: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.pce.2024.103561openalex-snapshot
Prediction enhancement for surface water sodium adsorption ratio using limited inputs: Implementation of hybridized stacked ensemble model with feature selection algorithm
inferred← citeshttps://doi.org/10.1016/j.powtec.2026.122729openalex-snapshot
Machine learning-based prediction of the thermal conductivity of hybrid nanofluids under varying operating conditions
inferred← citeshttps://doi.org/10.1016/j.sasc.2023.200049openalex-snapshot
Groundwater Levels Forecasting Using Machine Learning Models: A Case Study of the Groundwater Region 10 at Karst Belt, South Africa
inferred← citeshttps://doi.org/10.1016/j.sesci.2025.100261openalex-snapshot
Application of machine learning in python for temporal groundwater level prediction
inferred← citeshttps://doi.org/10.1016/j.still.2025.106880openalex-snapshot
The stacking method enhances machine learning models for monitoring and understanding regional soil available nitrogen variations in croplands
inferred← citeshttps://doi.org/10.1029/2024wr038238openalex-snapshot
Prediction of the Annual Variation of Groundwater Depth and Its Probability Based on MCAR Model and Copula Functions: A Case Study in Beijing, China
inferred← citeshttps://doi.org/10.1061/jhyeff.heeng-6280openalex-snapshot
Data-Driven Gray Box Modeling for Predicting Basin-Scale Groundwater Variations in Central Taiwan
inferred← citeshttps://doi.org/10.1080/1573062x.2026.2649885openalex-snapshot
Hybrid ensemble machine learning for groundwater level prediction in data-scarce northern Bangladesh
inferred← citeshttps://doi.org/10.1109/access.2021.3094735openalex-snapshot
Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management
inferred← citeshttps://doi.org/10.1109/access.2023.3298225openalex-snapshot
Predictive Modeling of Water Table Depth, Drilling Duration, and Soil Layer Classification Using Adaptive Ensemble Learning for Cost-Effective Percussion Water Borehole Drilling
inferred← citeshttps://doi.org/10.1109/access.2024.3422317openalex-snapshot
Hybridizing Artificial Neural Networks Through Feature Selection Based Supervised Weight Initialization and Traditional Machine Learning Algorithms for Improved Colon Cancer Prediction
inferred← citeshttps://doi.org/10.1109/access.2024.3518574openalex-snapshot
Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models
inferred← citeshttps://doi.org/10.1109/aidas67696.2025.11212852openalex-snapshot
Evaluating Statistical and Machine Learning Models for River Flow Forecasting in Terengganu: A Case Study Using Facebook Prophet, XGBoost, and Random Forest
inferred← citeshttps://doi.org/10.1109/eais51927.2022.9787530openalex-snapshot
Transformers-based time series forecasting for piezometric level prediction
inferred← citeshttps://doi.org/10.1109/iccr61006.2024.10533046openalex-snapshot
PDF Malware Detection: A Hybrid Approach Using Random Forest and K-Nearest Neighbors
inferred← citeshttps://doi.org/10.1109/icct67028.2025.11427802openalex-snapshot
Software Application for Identifying the Location of Small Hydropower Plants, Taking into Account the Landscape and Climatic Characteristics of the Region
inferred← citeshttps://doi.org/10.1109/icosst60641.2023.10414232openalex-snapshot
Forecasting Groundwater Depletion in Quetta Sub-Basin using Remote Sensing Data: Deep Learning Approach
inferred← citeshttps://doi.org/10.1109/icscna58489.2023.10370274openalex-snapshot
Enhancing Student Performance through Adaptive Knowledge Assessment: A Bayesian Optimization Approach
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8119285/v1openalex-snapshot
Denoising of Groundwater Level Data Using Wavelet Transform, SSA, and VMD: A Python-Based Approach
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9082298/v1openalex-snapshot
Machine Learning Prediction of Iron Supplement Utilization Among Pregnant Women in Somaliland: Evidence from the Somaliland Demographic and Health Survey 2020
inferred← citeshttps://doi.org/10.2139/ssrn.4851171openalex-snapshot
A Gray-Box Modeling Approach for Predicting Groundwater Levels and Analyzing Hydro-Geological Processes in the Chou-Shui Chi Alluvial Fan
inferred← citeshttps://doi.org/10.2139/ssrn.6283648openalex-snapshot
Spatiotemporal Prediction of Groundwater Fluctuations: A Multi-Model Approach to Analyze Climate Change Impacts
inferred← citeshttps://doi.org/10.2166/hydro.2025.290openalex-snapshot
Advanced machine learning models for robust prediction of water quality index and classification
inferred← citeshttps://doi.org/10.2166/ws.2023.248openalex-snapshot
A hybrid coupling model of groundwater level simulation considering hydrogeological parameter: a case study of Nantong City in Eastern China
inferred← citeshttps://doi.org/10.28989/avitec.v7i1.2722openalex-snapshot
Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption
inferred← citeshttps://doi.org/10.3389/feart.2022.1102802openalex-snapshot
Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model
inferred← citeshttps://doi.org/10.3389/fenvs.2024.1291327openalex-snapshot
Predicting groundwater level using traditional and deep machine learning algorithms
inferred← citeshttps://doi.org/10.3390/app13042743openalex-snapshot
A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
inferred← citeshttps://doi.org/10.3390/fluids7010020openalex-snapshot
Air Entrainment in Drop Shafts: A Novel Approach Based on Machine Learning Algorithms and Hybrid Models
inferred← citeshttps://doi.org/10.3390/hydrology10020029openalex-snapshot
ML-Based Streamflow Prediction in the Upper Colorado River Basin Using Climate Variables Time Series Data
inferred← citeshttps://doi.org/10.3390/hydrology13010001openalex-snapshot
Robust Rainfall Gap-Filling in Coastal Arid Regions Using Ensemble Fusion Models
inferred← citeshttps://doi.org/10.3390/hydrology9070125openalex-snapshot
A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa
inferred← citeshttps://doi.org/10.3390/jmse10070840openalex-snapshot
Ship Pitch Prediction Based on Bi-ConvLSTM-CA Model
inferred← citeshttps://doi.org/10.3390/min12060689openalex-snapshot
Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran
inferred← citeshttps://doi.org/10.3390/rs14215515openalex-snapshot
Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia
inferred← citeshttps://doi.org/10.3390/rs18101622openalex-snapshot
Estimating Regional Groundwater Level by Combining Satellite, Model, and Large-Sample Observations Inputs
inferred← citeshttps://doi.org/10.3390/su15053874openalex-snapshot
Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco
inferred← citeshttps://doi.org/10.3390/su17093851openalex-snapshot
Machine Learning-Driven Groundwater Potential Zoning Using Geospatial Analytics and Random Forest in the Pandameru River Basin, South India
inferred← citeshttps://doi.org/10.3390/su17198544openalex-snapshot
Groundwater Level Prediction Using a Hybrid TCN–Transformer–LSTM Model and Multi-Source Data Fusion: A Case Study of the Kuitun River Basin, Xinjiang
inferred← citeshttps://doi.org/10.3390/w14081230openalex-snapshot
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
inferred← citeshttps://doi.org/10.3390/w15203624openalex-snapshot
Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms
inferred← citeshttps://doi.org/10.3390/w15223940openalex-snapshot
A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction
inferred← citeshttps://doi.org/10.3390/w16223273openalex-snapshot
Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models
inferred← citeshttps://doi.org/10.3390/w17142064openalex-snapshot
GIS-Based Spatial Autocorrelation and Multivariate Statistics for Understanding Groundwater Uranium Contamination and Associated Health Risk in Semiarid Region of Punjab, India
inferred← citeshttps://doi.org/10.36548/jscp.2024.1.005openalex-snapshot
AI-Driven Groundwater Level Enhancement System using Advanced Prediction Algorithms
inferred← citeshttps://doi.org/10.4018/979-8-3373-4422-5.ch005openalex-snapshot
AI-Driven Solutions for Monitoring and Managing Groundwater Pollution
inferred← citeshttps://doi.org/10.51526/kbes.2023.4.1.55-77openalex-snapshot
Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model
inferred← citeshttps://doi.org/10.54392/irjmt26328openalex-snapshot
Identifying Soil Profiles and Groundwater Quality using Machine Learning Models with and without Clustering Methods
inferred← citeshttps://doi.org/10.9719/eeg.2024.57.5.551openalex-snapshot
Applying Transfer Learning to Improve the Performance of Deep Learning–based Groundwater Level Prediction Model with Insufficient Training Data
inferred← citeshttps://doi.org/10.9781/ijimai.2024.04.002openalex-snapshot
Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques.
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.