On the public citation graph

Modelling groundwater levels in an urban coastal aquifer using artificial neural networks

0 signed · 140 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/hyp.445openalex-snapshot
Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection
inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1007/s11269-006-4007-zopenalex-snapshot
Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach
inferredcites →https://doi.org/10.1016/0022-1694(70)90255-6openalex-snapshot
River flow forecasting through conceptual models part I — A discussion of principles
inferredcites →https://doi.org/10.1016/j.jhydrol.2004.10.005openalex-snapshot
On the use of neural networks to evaluate groundwater levels in fractured media
inferredcites →https://doi.org/10.1016/j.jhydrol.2004.12.001openalex-snapshot
Groundwater level forecasting using artificial neural networks
inferredcites →https://doi.org/10.1016/s0022-1694(00)00344-9openalex-snapshot
Comparison of short-term rainfall prediction models for real-time flood forecasting
inferredcites →https://doi.org/10.1016/s0022-1694(01)00606-0openalex-snapshot
Predicting average annual groundwater levels from climatic variables: an empirical model
inferredcites →https://doi.org/10.1016/s1364-8152(98)00019-xopenalex-snapshot
Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study
inferredcites →https://doi.org/10.1023/a:1008132509589openalex-snapshot
A Genetic Programming Approach to Rainfall-Runoff Modelling
inferredcites →https://doi.org/10.1023/b:narr.0000007808.11860.7eopenalex-snapshot
Application of Artificial Neural Networks to Complex Groundwater Management Problems
inferredcites →https://doi.org/10.1029/1999wr900288openalex-snapshot
Physical basis of time series models for water table depths
inferredcites →https://doi.org/10.1029/2000wr900368openalex-snapshot
Artificial neural network modeling of water table depth fluctuations
inferredcites →https://doi.org/10.1029/2003wr002355openalex-snapshot
Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques
inferredcites →https://doi.org/10.1029/98wr02298openalex-snapshot
Modeling water table fluctuations by means of a stochastic differential equation
inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1061/(asce)1084-0699(2000)5:2(115)openalex-snapshot
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
inferredcites →https://doi.org/10.1061/(asce)1084-0699(2000)5:2(124)openalex-snapshot
Artificial Neural Networks in Hydrology. II: Hydrologic Applications
inferredcites →https://doi.org/10.1061/(asce)1084-0699(2000)5:2(156)openalex-snapshot
Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models
inferredcites →https://doi.org/10.1061/(asce)1084-0699(2001)6:5(367)openalex-snapshot
Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks
inferredcites →https://doi.org/10.1061/(asce)1084-0699(2003)8:6(348)openalex-snapshot
Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions
inferredcites →https://doi.org/10.1111/j.1745-6584.2005.0003.xopenalex-snapshot
A neural network model for predicting aquifer water level elevations

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4750568openalex-extracted
Spatial and Temporal Forecasting of Groundwater Levels
inferred← citeshttps://doi.org/10.1002/9781119761686.ch2openalex-snapshot
Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource
inferred← citeshttps://doi.org/10.1002/hyp.10166openalex-snapshot
Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater
inferred← citeshttps://doi.org/10.1002/hyp.7352openalex-snapshot
Auto‐configuring radial basis function networks for chaotic time series and flood forecasting
inferred← citeshttps://doi.org/10.1002/hyp.7410openalex-snapshot
Optimal selection of artificial neural network parameters for the prediction of a karstic aquifer's response
inferred← citeshttps://doi.org/10.1002/hyp.8044openalex-snapshot
Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response
inferred← citeshttps://doi.org/10.1007/978-3-319-12559-6_6openalex-snapshot
Forecasting Groundwater Level Using Hybrid Modelling Technique
inferred← citeshttps://doi.org/10.1007/978-981-13-9282-5_76openalex-snapshot
Infiltration Loss Affects Toward Groundwater Fluctuation Through CANFIS in Arid Watershed: A Case Study
inferred← citeshttps://doi.org/10.1007/s00477-023-02570-6openalex-snapshot
A deep neural multi-model ensemble (DNM2E) framework for modelling groundwater levels over Kerala using dynamic variables
inferred← citeshttps://doi.org/10.1007/s00521-014-1794-7openalex-snapshot
Wavelet and adaptive neuro-fuzzy inference system conjunction model for groundwater level predicting in a coastal aquifer
inferred← citeshttps://doi.org/10.1007/s00521-016-2713-xopenalex-snapshot
Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction
inferred← citeshttps://doi.org/10.1007/s10040-013-1029-5openalex-snapshot
Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment
inferred← citeshttps://doi.org/10.1007/s10040-014-1204-3openalex-snapshot
Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models
inferred← citeshttps://doi.org/10.1007/s10040-017-1658-1openalex-snapshot
Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)
inferred← citeshttps://doi.org/10.1007/s10346-017-0844-5openalex-snapshot
Feasibility of using artificial neural networks to forecast groundwater levels in real time
inferred← citeshttps://doi.org/10.1007/s10661-012-2874-8openalex-snapshot
Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China
inferred← citeshttps://doi.org/10.1007/s10661-014-3650-8openalex-snapshot
Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater
inferred← citeshttps://doi.org/10.1007/s11269-009-9527-xopenalex-snapshot
Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India
inferred← citeshttps://doi.org/10.1007/s11269-014-0616-0openalex-snapshot
Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods
inferred← citeshttps://doi.org/10.1007/s11269-015-1132-6openalex-snapshot
Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites
inferred← citeshttps://doi.org/10.1007/s11269-017-1811-6openalex-snapshot
Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China
inferred← citeshttps://doi.org/10.1007/s11269-019-02253-4openalex-snapshot
Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System
inferred← citeshttps://doi.org/10.1007/s11269-021-02969-2openalex-snapshot
Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/s11356-022-23686-2openalex-snapshot
Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data
inferred← citeshttps://doi.org/10.1007/s11600-023-01050-3openalex-snapshot
A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer
inferred← citeshttps://doi.org/10.1007/s11831-025-10447-wopenalex-snapshot
Application of Hybrid Machine Learning for Groundwater Level Prediction: A Comprehensive Review
inferred← citeshttps://doi.org/10.1007/s12145-020-00508-yopenalex-snapshot
Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study
inferred← citeshttps://doi.org/10.1007/s12517-017-2954-8openalex-snapshot
Groundwater depth predictions by GSM, RBF, and ANFIS models: a comparative assessment
inferred← citeshttps://doi.org/10.1007/s12517-019-4490-1openalex-snapshot
Groundwater depth monitoring and short-term prediction: applied to El Hamma aquifer system, southeastern Tunisia
inferred← citeshttps://doi.org/10.1007/s12517-021-07467-8openalex-snapshot
Hydro-geochemical evaluation and health risk assessment of groundwater in coastal regions: a case study of eastern Andhra Pradesh, South India
inferred← citeshttps://doi.org/10.1007/s12524-023-01789-8openalex-snapshot
Comparing the Performance of Machine Learning Algorithms for Groundwater Mapping in Delhi
inferred← citeshttps://doi.org/10.1007/s12665-015-4708-9openalex-snapshot
Neural networks for karst groundwater management: case of the Lez spring (Southern France)
inferred← citeshttps://doi.org/10.1007/s12665-015-5198-5openalex-snapshot
Application of integrated ARIMA and RBF network for groundwater level forecasting
inferred← citeshttps://doi.org/10.1007/s12665-016-5650-1openalex-snapshot
Development of a novel method to monitor the temporal change in the location of the freshwater–saltwater interface and time series models for the prediction of the interface
inferred← citeshttps://doi.org/10.1007/s12665-017-6938-5openalex-snapshot
Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters
inferred← citeshttps://doi.org/10.1007/s12665-019-8776-0openalex-snapshot
Groundwater level prediction using genetic programming: the importance of precipitation data and weather station location on model accuracy
inferred← citeshttps://doi.org/10.1007/s13198-025-03116-5openalex-snapshot
Alternate groundwater modelling strategies: a multi-faceted data-driven approach
inferred← citeshttps://doi.org/10.1007/s13201-016-0508-yopenalex-snapshot
A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)
inferred← citeshttps://doi.org/10.1007/s13201-018-0886-4openalex-snapshot
A comparative study of the performance of artificial neural network and multivariate regression in simulating springs discharge in the Caspian Southern Watersheds, Iran
inferred← citeshttps://doi.org/10.1007/s40808-016-0083-0openalex-snapshot
Genetic programming based monthly groundwater level forecast models with uncertainty quantification
inferred← citeshttps://doi.org/10.1007/s42452-025-06817-5openalex-snapshot
A systematic review of neural network applications for groundwater level prediction
inferred← citeshttps://doi.org/10.1016/b978-0-12-818172-0.00040-2openalex-snapshot
Use of machine learning and deep learning methods in groundwater
inferred← citeshttps://doi.org/10.1016/j.cageo.2016.03.002openalex-snapshot
A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105227openalex-snapshot
Estimation of water table depth using DUALEM-2 system
inferred← citeshttps://doi.org/10.1016/j.enconman.2021.115160openalex-snapshot
Rational design of high power density “Blue Energy Harvester” pressure retarded osmosis (PRO) membranes using artificial intelligence-based modeling and optimization
inferred← citeshttps://doi.org/10.1016/j.envsoft.2023.105788openalex-snapshot
A systematic review and meta-analysis of groundwater level forecasting with machine learning techniques: Current status and future directions
inferred← citeshttps://doi.org/10.1016/j.gsd.2017.06.009openalex-snapshot
Wavelet based analysis on rainfall and water table depth forecasting using Neural Networks in Kanyakumari district, Tamil Nadu, India
inferred← citeshttps://doi.org/10.1016/j.gsd.2022.100882openalex-snapshot
Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways
inferred← citeshttps://doi.org/10.1016/j.gsd.2024.101174openalex-snapshot
Unraveling aquifer dynamics: Time series evaluation for informed groundwater management
inferred← citeshttps://doi.org/10.1016/j.gsd.2024.101213openalex-snapshot
Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2010.04.029openalex-snapshot
Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2010.11.002openalex-snapshot
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2011.06.013openalex-snapshot
A wavelet neural network conjunction model for groundwater level forecasting
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2013.04.041openalex-snapshot
Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2013.07.008openalex-snapshot
Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2015.05.034openalex-snapshot
Modeling soil water content in extreme arid area using an adaptive neuro-fuzzy inference system
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2015.09.028openalex-snapshot
Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2017.10.015openalex-snapshot
Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2018.12.037openalex-snapshot
A review of the artificial intelligence methods in groundwater level modeling
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2024.131525openalex-snapshot
Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change
inferred← citeshttps://doi.org/10.1016/j.pce.2023.103395openalex-snapshot
Modeling of standardized groundwater index of Bihar using machine learning techniques
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2021.145489openalex-snapshot
Enhancing the understanding of hydrological responses induced by ecological water replenishment using improved machine learning models: A case study in Yongding River
inferred← citeshttps://doi.org/10.1021/acsestwater.2c00220openalex-snapshot
Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia
inferred← citeshttps://doi.org/10.1061/(asce)he.1943-5584.0000218openalex-snapshot
Groundwater Level Prediction Using SOM-RBFN Multisite Model
inferred← citeshttps://doi.org/10.1061/(asce)he.1943-5584.0000892openalex-snapshot
Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting
inferred← citeshttps://doi.org/10.1080/02626667.2015.1055271openalex-snapshot
Improvement of artificial neural networks to predict daily streamflow in a semi-arid area
inferred← citeshttps://doi.org/10.1080/02626667.2017.1349908openalex-snapshot
Spatial estimation of water-table depth by artificial neural networks in light of ancillary data
inferred← citeshttps://doi.org/10.1080/09715010.2017.1360158openalex-snapshot
Forecasting stream flow using hybrid neuro-wavelet technique
inferred← citeshttps://doi.org/10.1088/1742-6596/2083/4/042064openalex-snapshot
Construction of elliptic stochastic partial differential equations solver in groundwater flow with convolutional neural networks
inferred← citeshttps://doi.org/10.1108/sasbe-06-2022-0123openalex-snapshot
Explaining resilience model of historical bazaars using artificial neural network
inferred← citeshttps://doi.org/10.1109/cscs59211.2023.00018openalex-snapshot
Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming
inferred← citeshttps://doi.org/10.1109/imitec50163.2020.9334142openalex-snapshot
Application of Machine Learning Techniques In Forecasting Groundwater Levels in the Grootfontein Aquifer
inferred← citeshttps://doi.org/10.1111/gwat.13156openalex-snapshot
Performance Analysis of ANN Prediction for Groundwater Level Considering Regional‐Specific Influence Components
inferred← citeshttps://doi.org/10.1111/gwat.13462openalex-snapshot
A Reduced Order Model for Sea Water Intrusion Simulation Using Proper Orthogonal Decomposition
inferred← citeshttps://doi.org/10.1142/9789814704830_0036openalex-snapshot
A Review of Application of Artificial Neural Network in Ground Water Modeling
inferred← citeshttps://doi.org/10.1155/2015/742138openalex-snapshot
Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS
inferred← citeshttps://doi.org/10.1260/1708-5284.9.6.509openalex-snapshot
Utilizing artificial neural network for forecasting groundwater table depths fluctuations
inferred← citeshttps://doi.org/10.13189/cea.2022.100304openalex-snapshot
Groundwater Level Forecasting Using Multiple Linear Regression and Artificial Neural Network Approaches
inferred← citeshttps://doi.org/10.1504/ijhst.2012.052366openalex-snapshot
Spatiotemporal groundwater level forecasting and monitoring using a neural network-based approach in a semi arid zone
inferred← citeshttps://doi.org/10.15171/ehem.2016.17openalex-snapshot
Integration of artificial neural network and geographic information system applications in simulating groundwater quality
inferred← citeshttps://doi.org/10.15171/ehem.2016.23openalex-snapshot
Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf
inferred← citeshttps://doi.org/10.15233/gfz.2015.32.9openalex-snapshot
Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)
inferred← citeshttps://doi.org/10.1680/wama.10.00092openalex-snapshot
Wavelet neural network model for river flow time series
inferred← citeshttps://doi.org/10.17577/ijertv8is070123openalex-snapshot
A Critical Review on Artificial Intelligence Models in Hydrological Forecasting How Reliable are Artificial Intelligence Models
inferred← citeshttps://doi.org/10.20546/ijcmas.2018.712.386openalex-snapshot
Simulation of Groundwater Level Using Recurrent Neural Network (RNN) in Raichur District, Karnataka
inferred← citeshttps://doi.org/10.2166/h2oj.2021.120openalex-snapshot
Statistical modeling of spatial and temporal vulnerability of groundwater level in the Gaza Strip (Palestine)
inferred← citeshttps://doi.org/10.2166/hydro.2010.034openalex-snapshot
Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran
inferred← citeshttps://doi.org/10.2166/nh.2017.153openalex-snapshot
Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models
inferred← citeshttps://doi.org/10.2175/106143012x13373575831637openalex-snapshot
Analysis of Groundwater Quality Behavior Based on a Statistical Approach: Case Study of Khan Yunis Governorate (Palestine)
inferred← citeshttps://doi.org/10.31223/x5vx1vopenalex-snapshot
A Systematic Review of Neural Network Applications for Groundwater Level Prediction
inferred← citeshttps://doi.org/10.3233/kes-210066openalex-snapshot
Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques
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/app14167358openalex-snapshot
Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review
inferred← citeshttps://doi.org/10.3390/books978-3-03921-198-2openalex-snapshot
Groundwater Resources and Salt Water Intrusion in a Changing Environment
inferred← citeshttps://doi.org/10.3390/math10010107openalex-snapshot
Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks
inferred← citeshttps://doi.org/10.3390/rs10010143openalex-snapshot
Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley
inferred← citeshttps://doi.org/10.3390/su13147752openalex-snapshot
A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model
inferred← citeshttps://doi.org/10.3390/w12010005openalex-snapshot
Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning
inferred← citeshttps://doi.org/10.3390/w12082107openalex-snapshot
Simulation of Seawater Intrusion Area Using Feedforward Neural Network in Longkou, China
inferred← citeshttps://doi.org/10.3390/w15040801openalex-snapshot
Comparison of Three Imputation Methods for Groundwater Level Timeseries
inferred← citeshttps://doi.org/10.3390/w15050972openalex-snapshot
A Case Study: Groundwater Level Forecasting of the Gyorae Area in Actual Practice on Jeju Island Using Deep-Learning Technique
inferred← citeshttps://doi.org/10.3390/w9050323openalex-snapshot
Comparative Evaluation of ANN- and SVM-Time Series Models for Predicting Freshwater-Saltwater Interface Fluctuations
inferred← citeshttps://doi.org/10.3390/w9100781openalex-snapshot
Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.02openalex-snapshot
Qualidade da água em trecho do Eixo Leste do Projeto de Transposição do Rio São Francisco, Brasil
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.03openalex-snapshot
Modelagem no tratamento de água: Influência do gradiente de velocidade
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.05openalex-snapshot
Estudos sobre a água do ar condicionado
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.08openalex-snapshot
Intervenções ocorridas no Teatro São João no contexto do Sítio Histórico de Sobral
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.09openalex-snapshot
As possibilidades de atuação do engenheiro de produção na Modelagem da Informação da Construção (BIM)
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.11openalex-snapshot
Ensaios de moagem do feldspato pegmatítico do Alto Serra Branca-PB
inferred← citeshttps://doi.org/10.36229/978-65-86127-91-1.cap.12openalex-snapshot
Estudo do efeito inibidor das cinzas de carvão mineral na corrosão do aço embutido em argamassa em meio a íons sulfato
inferred← citeshttps://doi.org/10.4236/gep.2017.53008openalex-snapshot
Application of ANN and MLR Models on Groundwater Quality Using CWQI at Lawspet, Puducherry in India
inferred← citeshttps://doi.org/10.4236/jwarp.2011.31006openalex-snapshot
Time Series Modeling of River Flow Using Wavelet Neural Networks
inferred← citeshttps://doi.org/10.5194/essd-18-77-2026openalex-snapshot
GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features
inferred← citeshttps://doi.org/10.5897/ajar11.1892openalex-snapshot
Groundwater modeling using hybrid of artificial neural network with genetic algorithm
inferred← citeshttps://doi.org/10.5897/ijwree.9000045openalex-snapshot
Prediction of water table depth in a hard rock basin by using artificial neural network
inferred← citeshttps://doi.org/10.5942/jawwa.2015.107.0073openalex-snapshot
Use of Artificial Neural Networks and Genetic Algorithms in Urban Water Management: A Brief Overview
inferred← citeshttps://doi.org/10.70675/1ae0eb31zd379z4eeaz9e98z34f1ff798bd5openalex-snapshot
Apport des réseaux de neurones artificiels dans la prévision du risque de crue karstique sur le site du Commissariat à l’Energie Atomique de Cadarache
inferred← citeshttps://doi.org/10.9734/jsrr/2015/17870openalex-snapshot
Artificial Network for Predicting Water Uptake under Shallow Saline Ground Water Conditions
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.