Theo dơi
Sungwon Kim
Sungwon Kim
Professor, Dept. of Railroad Construction and Safety Engineering, Dongyang University
Email được xác minh tại dyu.ac.kr - Trang chủ
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
Daily water level forecasting using wavelet decomposition and artificial intelligence techniques
Y Seo, S Kim, O Kisi, VP Singh
Journal of Hydrology 520, 224-243, 2015
3282015
Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling
S Kim, HS Kim
Journal of Hydrology 351 (3-4), 299-317, 2008
2962008
Groundwater level prediction using machine learning models: A comprehensive review
Neurocomputing 489, 271-308, 2022
2152022
Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach
ZM Yaseen, MT Tran, S Kim, T Bakhshpoori, RC Deo
Engineering Structures 177, 244-255, 2018
1352018
A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
M Alizamir, S Kim, O Kisi, M Zounemat-Kermani
Energy 197, 117239, 2020
1332020
Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model
A Malik, A Kumar, S Kim, MH Kashani, V Karimi, A Sharafati, MA Ghorbani, ...
Engineering Applications of Computational Fluid Mechanics 14 (1), 323-338, 2020
1122020
Drought forecasting using novel heuristic methods in a semi-arid environment
O Kisi, AD Gorgij, M Zounemat-Kermani, A Mahdavi-Meymand, S Kim
Journal of Hydrology 578, 124053, 2019
1122019
Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis
ZM Yaseen, I Ebtehaj, S Kim, H Sanikhani, H Asadi, MI Ghareb, ...
Water 11 (3), 502, 2019
1032019
Pan evaporation modeling using neural computing approach for different climatic zones
S Kim, J Shiri, O Kisi
Water Resources Management 26, 3231-3249, 2012
992012
River salinity prediction using hybrid machine learning models
AM Melesse, K Khosravi, JP Tiefenbacher, S Heddam, S Kim, A Mosavi, ...
Water 12 (10), 2951, 2020
982020
Modeling daily soil temperature using data-driven models and spatial distribution
S Kim, VP Singh
Theoretical and Applied Climatology 118, 465-479, 2014
982014
Advanced machine learning model for better prediction accuracy of soil temperature at different depths
M Alizamir, O Kisi, AN Ahmed, C Mert, CM Fai, S Kim, NW Kim, ...
PLoS ONE 15 (4), e0231055, 2020
942020
Estimating daily pan evaporation using different data-driven methods and lag-time patterns
S Kim, J Shiri, O Kisi, VP Singh
Water Resources Management 27, 2267-2286, 2013
922013
Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)
A Pour-Ali Baba, J Shiri, O Kisi, AF Fard, S Kim, R Amini
Hydrology Research 44 (1), 131-146, 2013
852013
Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea
M Rezaie-Balf, S Kim, H Fallah, S Alaghmand
Journal of Hydrology 572, 470-485, 2019
752019
Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida
M Zounemat-Kermani, Y Seo, S Kim, MA Ghorbani, S Samadianfard, ...
Applied Sciences 9 (12), 2534, 2019
732019
Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India
A Malik, A Kumar, SQ Salih, S Kim, NW Kim, ZM Yaseen, VP Singh
PLoS ONE 15 (5), e0233280, 2020
722020
Estimating spatial precipitation using regression kriging and artificial neural network residual kriging (RKNNRK) hybrid approach
Y Seo, S Kim, VP Singh
Water Resources Management 29, 2189-2204, 2015
712015
Machine learning models coupled with variational mode decomposition: a new approach for modeling daily rainfall-runoff
Y Seo, S Kim, VP Singh
Atmosphere 9 (7), 251, 2018
682018
Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia
MA Ghorbani, RC Deo, S Kim, MH Kashani, V Karimi, M Izadkhah
Soft Computing 24, 12079-12090, 2020
642020
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