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Construction and Building Materials, ISSN 0950-0618, 09/2015, Volume 94, pp. 137 - 147
Journal Article
Ocean Engineering, ISSN 0029-8018, 01/2016, Volume 112, pp. 76 - 81
In recent years, soft computing schemes have received increasing attention for solving coastal engineering problems and knowledge extraction from the existing... 
Nearshore hydrodynamics | M5′ algorithm | Wave runup | Model tree | ENGINEERING, CIVIL | NEURAL-NETWORKS | ENGINEERING, MARINE | ENGINEERING, OCEAN | OCEANOGRAPHY | SMOOTH | M5 ' algorithm | SLOPES | Models | Algorithms | Permeability | Analysis | Trees | Similarity | Flux | Ocean engineering | Mathematical models | Soft computing | Decision trees
Journal Article
Journal of Earthquake Engineering, ISSN 1363-2469, 08/2016, Volume 20, Issue 6, pp. 910 - 930
Journal Article
Applied Soft Computing Journal, ISSN 1568-4946, 07/2018, Volume 68, pp. 147 - 161
Journal Article
Water (Switzerland), ISSN 2073-4441, 2017, Volume 9, Issue 2, pp. 105 - 105
Stormwater runoff is often contaminated by human activities. Stormwater discharge into water bodies significantly contributes to environmental pollution. The... 
Treatment units | Regression Tree | Wastewater | Machine learning | Support Vector Regression | Water quality | wastewater | MANAGEMENT | WATER RESOURCES | machine learning | treatment units | PILE GROUPS SCOUR | PREDICTION | M5 MODEL TREES | NEURAL-NETWORKS | water quality | RUNOFF | Regression Tree | treatment units | Support Vector Regression | machine learning | water quality
Journal Article
Journal Article
Engineering Failure Analysis, ISSN 1350-6307, 03/2019, Volume 97, pp. 793 - 803
In this paper, the failure probability of corroded pipelines made by X60 steel grade is evaluated. For this complex real engineering failure problem, the burst... 
Reliability analysis | X60 steel | Monte Carlo | External corrosion | M5 model tree algorithm | Corroded pipeline | SYSTEM RELIABILITY | ROBUST | STRUCTURAL RELIABILITY | MATERIALS SCIENCE, CHARACTERIZATION & TESTING | PROBABILISTIC MODEL | ENGINEERING, MECHANICAL
Journal Article
WATER, ISSN 2073-4441, 09/2019, Volume 11, Issue 9, p. 1934
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine... 
STREAM | WATER RESOURCES | hybrid machine learning | M5 model tree | river flow forecasting | stream flow | hydro-informatics | PREDICTION | support vector regression | deep learning | NEURAL-NETWORKS | fruit fly optimization algorithm (FOA) | big data
Journal Article
International Journal of Applied Earth Observation and Geoinformation, ISSN 1569-8432, 12/2018, Volume 73, pp. 503 - 521
Journal Article
Advances in Intelligent Systems and Computing, ISSN 2194-5357, 2018, Volume 563, pp. 425 - 432
Conference Proceeding
Journal of Hydrologic Engineering, ISSN 1084-0699, 03/2012, Volume 17, Issue 3, pp. 394 - 404
The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High... 
TECHNICAL PAPERS | M5 | Suspended sediment concentration | Neural networks | Fuzzy Logic | Bhakra reservoir | REPTree | LOAD | WATER RESOURCES | RIVER | EROSION | ARTIFICIAL NEURAL-NETWORKS | PREDICTION | WATERSHEDS | DISCHARGE | ENVIRONMENTAL SCIENCES | TRANSPORT | ENGINEERING, CIVIL | RUNOFF | Algorithms | Mathematical analysis | Mathematical models | Soft computing | Learning theory | Autocorrelation | Sediments
Journal Article