연구동향 분석

Improvement of groundwater contamination vulnerability assessment using the adaptive neuro-fuzzy inference system with metaheuristic optimization algorithms = 적응형 뉴로-퍼지와 메타휴리스틱 최적화 기법의 결합 모델을 이용한 지하수 오염취약성 평가기법의 개선

HUSSAM ELDIN ELZAIN OSMAN ELZAIN 2020년

논문상세정보
인용/피인용
' Improvement of groundwater contamination vulnerability assessment using the adaptive neuro-fuzzy inference system with metaheuristic optimization algorithms = 적응형 뉴로-퍼지와 메타휴리스틱 최적화 기법의 결합 모델을 이용한 지하수 오염취약성 평가기법의 개선' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • DRASTIC assessment
  • Groundwater contamination vulnerability
  • MOA
  • anfis
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
40 0

0.0%

' Improvement of groundwater contamination vulnerability assessment using the adaptive neuro-fuzzy inference system with metaheuristic optimization algorithms = 적응형 뉴로-퍼지와 메타휴리스틱 최적화 기법의 결합 모델을 이용한 지하수 오염취약성 평가기법의 개선' 의 참고문헌

  • for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), pp.361-378.
  • Wang, L., Stuart, M.E., Bloomfield, J.P., Butcher, A.S., Gooddy, D.C., McKenzie, A.A., Lewis, M.A. and Williams, A.T., 2012. Prediction of the arrival of peak nitrate concentrations at the water table at the regional scale in Great Britain. Hydrological Processes, 26(2), pp.226-239.
  • Vulnerability of groundwater resources to nitrate pollution : A simple and effective procedure for delimiting Nitrate Vulnerable Zones
    575 , pp.799-812 . [2017]
  • Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping .
    581 , p.124379 [2020]
  • Using genetic algorithms to optimize the analogue method for precipitation prediction in the Swiss Alps
    [2018]
  • Understanding the impacts of groundwater abstraction through long-term trends in water quality
    156 , pp.241-251 [2019]
  • The long term effect of agricultural , vadose zone and climatic factors on nitrate contamination in the Nebraska 's groundwater system .
    220 , pp.33-48 . [2019]
  • The application of computational intelligence to landslide susceptibility mapping in Turkey
    [2005]
  • Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty
    26 ( 8 ) , 999–1008 .
  • Specific vulnerability assessment of nitrate in shallow groundwater with an improved DRSTIC-LE model .
    174 , pp.649-657 . [2019]
  • Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles
    19 ( 21 ) , p.4698 [2019]
  • Simulation optimization model for aquifer parameter estimation usingCoupled meshfree pointCollocation method andCat swarm optimization
    91 , pp.60-72 [2018]
  • Sensitivity analysis for the EPIK method of vulnerability assessment in a small karstic aquifer , southern Belgium
    8 ( 3 ) , pp.337-345 . [2000]
  • SWAT parameterization for the identification of critical diffuse pollution source areas under data limitations
    222 ( 19 ) , pp.3500-3512 [2011]
  • Reliability , resiliency , and vulnerabilityCriteria for water resource system performance evaluation
    18 ( 1 ) , pp.14-20 . [1982]
  • Quantity and quality of groundwater in theCampo de dalias ( almeria , Se Spain )
    24 ( 11 ) , 87e96 . [2015]
  • Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability : ACase study in an agricultural setting ( Southern Spain )
    [2014]
  • Predicting groundwater nitrateConcentrations in a region of mixed agricultural land use : aComparison of three approaches
    115 ( 2 ) , pp.191-204 [2001]
  • Predicting ground water nitrateConcentration from land use.
    43 ( 3 ) , pp.343-352 . [2005]
  • Predicted nitrate and arsenicConcentrations in basin-fill aquifers of the southwestern United States
    [2012]
  • Particle swarm optimization
    [2010]
  • Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS
    14 ( 6 ) , pp.894-911 [2006]
  • Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer , Iran
    503 , pp.89-100 [2013]
  • Nitrate pollution of groundwater ; all right… , but nothing else ?
    539 , pp.241-251 [2016]
  • Neural network modelling for nitrate concentration in groundwater of Kadava River basin , Nashik , Maharashtra , India
    7 , pp.436-445 . [2018]
  • Nature-inspired optimization algorithms .
    [2014]
  • Nature-inspired algorithms and applied optimization
    Vol . 744 ) [2017]
  • Modification to the DRASTIC framework to assess groundwater contaminant risk in rural mountainous catchments
    566 , pp.175-191 [2018]
  • Modeling river water quality parameters using modified adaptive neuro fuzzy inference system
    12 ( 1 ) , pp.45-54
  • Modeling of Tropospheric Delays using ANFIS
    109 p . [2016]
  • Modeling nitrate at domestic and public-supply well depths in the Central Valley ,
    48 ( 10 ) , pp.5643-5651 . [2014]
  • Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study
    655 , pp.512-519 [2019]
  • Modeling groundwater nitrate concentrations in private wells in Iowa
    536 , pp.481-488 [2015]
  • Moayedi, H., Mehrabi, M., Kalantar, B., Abdullahi Mu’azu, M., A. Rashid, A.S., Foong, L.K. and Nguyen, H., 2019. Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide. Geomatics, Natural Hazards and Risk, 10(1), pp.1879-1911.
  • Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater ,
    559 , pp.428-441 . [2018]
  • Meta-heuristic algorithms in optimizing GALDIT framework : A comparative study for coastal aquifer vulnerability assessment .
    [2020]
  • Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures
    593 , pp.75-90 [2017]
  • Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks
    227 , pp.415-428 [2018]
  • Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms .
    621 , pp.697-712 [2018]
  • Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning .
    668 , pp.1317-1327 [2019]
  • Joint inference of groundwater–recharge and hydraulic– conductivity fields from head data using the ensemble Kalman filter
    20 ( 1 ) , pp.555-569 . [2016]
  • J. and Eberhart R. , Particle swarm optimization
    vol . 4 , [1995]
  • Improved particle swarm algorithm for hydrological parameter optimization
    217 ( 7 ) , pp.3207- 3215 [2010]
  • Identification of the optimum groundwater quality monitoring network using a genetic algorithm-based optimization approach
    563 , pp.1078-1091 [2018]
  • Hydrological and pollution processes in mining area of Fenhe River Basin in China
    234 , pp.743-750 [2018]
  • Hybridized neural fuzzy ensembles for dust source modeling and prediction
    224 , p.117320 [2020]
  • Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs
    569 , pp.12-21 [2019]
  • Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability
    266 , pp.198-207 [2019]
  • Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS
    540 , pp.317-330
  • Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS
    540 , pp.317-330 [2016]
  • Groundwater vulnerability indices conditioned by supervised intelligence committee machine ( SICM )
    574 , pp.691-706 [2017]
  • Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel 's Sharon region
    54 ( 1 ) , pp.39-57 [1998]
  • Groundwater vulnerability and risk mapping using GIS , modeling and a fuzzy logic tool
    94 ( 3-4 ) , pp.277-292 [2007]
  • Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate : modifying the DRASTIC method using quantitative parameters .
    525 , pp.13-25 . [2015]
  • Groundwater risk assessment based on optimization framework using DRASTIC method
    9 ( 20 ) , p.742 . [2016]
  • Groundwater Vulnerability Map Explanatory Notes , Lachlan Catchment , Centre of Natural Resources . NewSouthWales ( NSW ) Department of Land and Water Conservation
    p. 14 [2001]
  • Groundwater DRASTIC vulnerability mapping by unsupervised and supervised techniques using a modelling strategy in two levels
    574 , pp.744-759 [2019]
  • Goodness-of-fit indices for partial least squares path modeling
    28 ( 2 ) , pp.565-580 [2013]
  • Geostatistical techniques to evaluate groundwater contamination and its sources in Miryang City , Korea
    75 ( 11 ) , p.994 . [2016]
  • GIS-based DRASTIC , Pesticide DRASTIC and the Susceptibility Index ( SI ) : comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer , Tunisia
    21 ( 3 ) , pp.715-731 . [2013]
  • GIS and Geocomputation for Water Resource Science and Engineering
    [2016]
  • Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility
    260 , p.109867 [2020]
  • Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination .
    96 , pp.146-157 . [2017]
  • Fuzzy Sets ''
    8 ( 3 ) , 338–353 [1965]
  • Fundamental concepts in aquifer vulnerability , pollution risk and protection strategy
    [1987]
  • Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms
    615 , pp.438-451 [2018]
  • Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution
    621 , pp.1124-1141 [2018]
  • Feature selection approaches for predictive modelling of groundwater nitrate pollution : An evaluation of filters , embedded and wrapper methods
    624 , pp.661-672 [2018]
  • Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting
    36 ( 3 ) , pp.4523- 4527 . [2009]
  • Evolutionary and swarm intelligence algorithms .
    [2019]
  • Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinization and nitrate contamination levels in two agricultural regions in the south of Portugal .
    14 ( 1-2 ) , pp.79-99 . [2006]
  • Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm
    [2020]
  • Effect of hydrogeological conditions and surface loads on shallow groundwater nitrate pollution in the Shaying River Basin : Based on least squares surface fitting model
    163 , p.114880 . [2019]
  • Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
    11 ( 4 ) , pp.341-359 [1997]
  • Differential Evolution : A survey of theoretical analyses
    44 , pp.546-558 [2019]
  • Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates , based on the drastic model , an example from NE Korinthia
    333 ( 2-4 ) , pp.288-304 [2007]
  • Development and application of a novel method for regional assessment of groundwater contamination risk in the Songhua River Basin .
    605 , pp.598-609 [2017]
  • Developing robust arsenic awareness prediction models using machine learning algorithms .
    211125-137 [2018]
  • Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks
    26 ( 8 ) , pp.8325-8339 . [2019]
  • DRASTIC-A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings : Ada , Okla. , Robert S. Ken-Environmental Research Laboratory
    EPA/600/2-87-035 , Volumes 1 and 2 . [1987]
  • Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods
    39 ( 6 ) , pp.549- 559 . [2000]
  • Comparison of adaptive neuro-fuzzy inference system ( ANFIS ) and Gaussian processes for machine learning ( GPML ) algorithms for the prediction of skin temperature in lower limb prostheses
    38 ( 10 ) , pp.1083-1089 [2016]
  • Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling
  • Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling
    [2019]
  • Calibration of groundwater vulnerability mapping using the generalized reduced gradient method
    207 , pp.39-49 [2017]
  • Buvaneshwari, S., Riotte, J., Sekhar, M., Kumar, M.M., Sharma, A.K., Duprey, J.L., Audry, S., Giriraja, P.R., Praveenkumarreddy, Y., Moger, H. and Durand, P., 2017. Groundwater resource vulnerability and spatial variability of nitrate contamination: insights from high density tubewell monitoring in a hard rock aquifer. Science of the Total Environment, 579, pp.838-847.
  • Bui , D.T. , 2019 . Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater
    Science of the
  • B. and Revhaug , I. , 2016b . Spatial prediction models
  • Automatic calibration a hydrological model using a master– slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization
    40 ( 2 ) , pp.752-757 . [2013]
  • Assessment and validation of groundwater vulnerability to nitrate in porous aquifers based on a DRASTIC method modified by projection pursuit dynamic clustering model
    226 , p.103522 [2019]
  • Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model : a case study in Jilin City of northeast China
    440 , pp.14-23 [2012]
  • Assessment and mapping of groundwater vulnerability to pollution : Current status and challenges
    185 , pp.901-927 [2018]
  • Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River , Isfahan , Iran .
    21 ( 1 ) , pp.134-140 . [2017]
  • Appraisal of groundwater with special reference to nitrate using statistical index approach
    8 , pp.49-58 . [2019]
  • Application of adaptive neuro-fuzzy inference system and data mining approach to predict lost circulation using DOE technique ( Case study : Maroon oilfield )
    [2018]
  • Application of Dempster–Shafer theory , spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan ,
    [2016]
  • An introduction to roc analysis
    27 , 861-874 . [2006]
  • An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment
    76 ( 1 ) , pp.543- 563 [2015]
  • An Introduction to Variable and Feature Selection
    3 , 1157–1182 [2003]
  • Algorithmic design issues in adaptive differential evolution schemes : Review and taxonomy
    43 , pp.284-311 [2018]
  • Adaptive neuro-fuzzy inference system for prediction of water level in reservoir
    29 ( 1 ) , pp.1-10 [2006]
  • Adaptive network based fuzzy inference system with meta-heuristic optimizations for international roughness index prediction
    9 ( 21 ) , p.4715 . [2019]
  • Adaptation in Natural and Artificial Systems : An Introductory Analysis With Applications to Biology , Control , and Artificial Intelligence
    [1992]
  • ANFIS : adaptive-network-based fuzzy inference system .
    23 ( 3 ) , pp.665-685 . [1993]
  • A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk : a case study from Tabriz plain aquifer
    30 ( 3 ) , pp.883-899 [2016]
  • A statistical learning framework for groundwater nitrate models of the Central Valley ,
    531 , pp.902-911 [2015]
  • A split-step particle swarm optimization algorithm in river stage forecasting
    346 ( 3-4 ) , pp.131-135 [2007]
  • A review of the artificial intelligence methods in groundwater level modeling
    572 , pp.336-351 [2019]
  • A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in southwest China
    70 , pp.562-575 [2018]
  • A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination .
    [2018]
  • A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses
    [2019]
  • A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS
    14 ( 1 ) , pp.1-17
  • A hybrid genetic algorithm and particle swarm optimization for multimodal functions
    8 ( 2 ) , pp.849-857 [2008]
  • A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area
    233 , pp.32-44
  • A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment
    642 , pp.1032-1049 [2018]
  • A comparative study of artificial neural network , adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region
    509 , pp.379-386 [2014]
  • A caution regarding rules of thumb for variance inflation factors
    41 , 673–690 [2007]
  • A case study using support vector machines , neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Hydrogeology Journal
    17 ( 6 ) , pp.1507-1520 [2009]
  • A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights , Gifu Prefecture , central Japan .
    345 ( 1-3 ) , pp.127-140 . [2005]
  • 2018. Review of hybrid evolutionary algorithms for optimizing a reservoir
    25 , pp.22-31
  • . and Belitz , K. , 2017 . A hybrid machine learning model to predict and visualize
    USA . Science of