Suchergebnisse - "Hydrological Modeling using Machine Learning Methods"

Comparative evaluation of spatiotemporal variations of surface water quality using water quality indices and GIS

Autoren: Ayşenur Uslu Secil Tuzun Dugan Abdellah El Hmaidi et al.

Quelle: Earth Science Informatics. 17:4197-4212

Schlagwörter: Environmental Engineering, 0207 environmental engineering, Environmental engineering, Epistemology, 02 engineering and technology, 01 natural sciences, Environmental science, Filter (signal processing), Engineering, Hydrological Modeling using Machine Learning Methods, Water Quality Index, 11. Sustainability, Groundwater Quality Assessment, 14. Life underwater, Environmental resource management, Biology, Water Science and Technology, 0105 earth and related environmental sciences, Assessment of Surface Water Quality, 2. Zero hunger, Ecology, FOS: Environmental engineering, Groundwater Level Forecasting, Surface water, Hydrology (agriculture), Sampling (signal processing), 15. Life on land, Computer science, Mapping Groundwater Potential Zones Using GIS Techniques, Water resource management, 6. Clean water, FOS: Philosophy, ethics and religion, Philosophy, Geotechnical engineering, Water quality, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, Watershed Prioritization, Quality (philosophy), Computer vision, Surface Water Pollution

Zugangs-URL: https://aperta.ulakbim.gov.tr/record/284631

Prediction of the river water environment carrying capacity using LSTM networks

Autoren: Long Ta Bui Tran Ngoc Dang Dan Phuoc Nguyen

Quelle: Water Supply, Vol 24, Iss 7, Pp 2518-2533 (2024)

Schlagwörter: Environmental Engineering, Rainfall-Runoff Modeling, lstm, water quality, 01 natural sciences, 7. Clean energy, Environmental science, River, lake, and water-supply engineering (General), Context (archaeology), Meteorology, Engineering, Hydrological Modeling using Machine Learning Methods, FOS: Mathematics, Real-time Water Quality Monitoring and Aquaculture Management, Groundwater Quality Assessment, 14. Life underwater, TD201-500, 0105 earth and related environmental sciences, Water Science and Technology, TC401-506, Assessment of Surface Water Quality, Water supply for domestic and industrial purposes, Geography, hydrological models, Statistics, FOS: Environmental engineering, Groundwater Level Forecasting, Paleontology, Hydrology (agriculture), Geology, FOS: Earth and related environmental sciences, 15. Life on land, Computer science, 6. Clean water, eco-hydrodynamic, Geotechnical engineering, 13. Climate action, Environmental Science, Physical Sciences, 8. Economic growth, Mean squared error, river water environmental capacity, Water Quality Monitoring, Mathematics, Forecasting

Zugangs-URL: https://doaj.org/article/97c771f7e1144121bf52c23f8ab1ceb2

A comparative study on data mining models for weather forecasting: A case study on Chittagong, Bangladesh

Autoren: Mohammad Sadman Tahsin Shahriar Abdullah Musaddiq Al Karim et al.

Quelle: Natural Hazards Research, Vol 4, Iss 2, Pp 295-303 (2024)

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Environmental Engineering, Support vector machine, Rainfall-Runoff Modeling, Geophysics. Cosmic physics, Social Sciences, Receiver operating characteristic, Management Science and Operations Research, Weather forecasting, Decision Sciences, Forecasting Models, Hydrological Modeling using Machine Learning Methods, Data Mining Techniques and Applications, Machine learning, 11. Sustainability, Temporal Data Mining, 14. Life underwater, Weather prediction, Data mining, QE1-996.5, Precision and recall, Naive Bayes classifier, QC801-809, 4. Education, Temperature, FOS: Environmental engineering, Geology, Predicting Stock Market Trends and Movements, Cross-validation, 15. Life on land, Computer science, C4.5 algorithm, Confusion matrix, Model Performance, 13. Climate action, Environmental Science, Physical Sciences, Computer Science, Information Systems, Forecasting

Zugangs-URL: https://doaj.org/article/46e68f1a323841429b6fa4081f5734c1

Developing a machine learning-based flood risk prediction model for the Indus Basin in Pakistan

Autoren: Mehran Khan Afed Ullah Khan Baseer Ullah et al.

Quelle: Water Practice and Technology, Vol 19, Iss 6, Pp 2213-2225 (2024)

Schlagwörter: Artificial neural network, Artificial intelligence, Environmental Engineering, Support vector machine, Rainfall-Runoff Modeling, 0207 environmental engineering, Flood Risk, 02 engineering and technology, Environmental technology. Sanitary engineering, 01 natural sciences, Global Flood Risk Assessment and Management, Indus, Hydrological Modeling using Machine Learning Methods, Machine learning, 11. Sustainability, Biology, TD1-1066, 0105 earth and related environmental sciences, Global and Planetary Change, Naive Bayes classifier, Geography, FOS: Environmental engineering, Warning system, Groundwater Level Forecasting, Paleontology, modeling, prediction, flood, Flood myth, 15. Life on land, Computer science, Structural basin, 6. Clean water, 3. Good health, Surface Water Mapping, machine learning, Archaeology, 13. Climate action, Environmental Science, Physical Sciences, Global Drought Monitoring and Assessment, Telecommunications, streamflow, Flood Inundation Modeling, Random forest

Zugangs-URL: https://doaj.org/article/3558549e9395422dbc099ea1a6d3dec5

Assessment of climate change impact on inflows to Amandara headwork using HEC-HMS and ANNs

Autoren: Haider Ali Hassaan Ateeq ur Rauf Abdul Razzaq Ghumman et al.

Quelle: Journal of Umm Al-Qura University for Engineering and Architecture. 15:403-420

Schlagwörter: Environmental Engineering, Rainfall-Runoff Modeling, 0207 environmental engineering, 02 engineering and technology, Oceanography, 01 natural sciences, Environmental science, Global Flood Risk Assessment and Management, Hydrological Modeling using Machine Learning Methods, Climate change, 14. Life underwater, Water Science and Technology, 0105 earth and related environmental sciences, Global and Planetary Change, FOS: Environmental engineering, Groundwater Level Forecasting, Hydrology (agriculture), Geology, FOS: Earth and related environmental sciences, 15. Life on land, Water resource management, 6. Clean water, Geotechnical engineering, Hydrological Modeling and Water Resource Management, 13. Climate action, Environmental Science, Physical Sciences

Monthly streamflow forecasting based on meteorological data from a nearby station

Autoren: Ebrahim Nohani Ahmadreza Karimipour Sara Khazaei et al.

Quelle: Water Practice and Technology, Vol 19, Iss 5, Pp 1659-1675 (2024)

Schlagwörter: Cartography, smor, Environmental Engineering, Rainfall-Runoff Modeling, Drainage basin, 0208 environmental biotechnology, 0207 environmental engineering, forecasting, Streamflow, 02 engineering and technology, Environmental technology. Sanitary engineering, Environmental science, Meteorology, Hydrological Modeling using Machine Learning Methods, 14. Life underwater, iran, TD1-1066, Streamflow Trends, dagging, Water Science and Technology, Climatology, Geography, 4. Education, FOS: Environmental engineering, Groundwater Level Forecasting, Geology, data mining, FOS: Earth and related environmental sciences, 15. Life on land, Watershed Simulation, 6. Clean water, 3. Good health, Hydrological Modeling and Water Resource Management, 13. Climate action, Environmental Science, Physical Sciences, streamflow, Forecasting

Zugangs-URL: https://doaj.org/article/b09db5a3ee2c443c8a2a72a4e5426dec

Relative and Combined Impacts of Climate and Land Use/Cover Change for the Streamflow Variability in the Baro River Basin (BRB)

Autoren: Shimelash Molla Kassaye Tsegaye Tadesse Getachew Tegegne et al.

Quelle: Earth, Vol 5, Iss 2, Pp 149-168 (2024)

Schlagwörter: 0208 environmental biotechnology, FOS: Mechanical engineering, Flood Risk, 02 engineering and technology, Oceanography, Environmental technology. Sanitary engineering, land-use change, Engineering, Hydrological Modeling using Machine Learning Methods, Climate change, TD1-1066, Water Science and Technology, Climatology, Global and Planetary Change, SWAT+, Geography, Ecology, Groundwater Level Forecasting, Hydrology (agriculture), Geology, Structural basin, Mechanical engineering, 6. Clean water, Hydrological Modeling and Water Resource Management, Physical Sciences, Cartography, Land cover, Physical geography, Environmental Engineering, Rainfall-Runoff Modeling, Drainage basin, 0207 environmental engineering, Streamflow, land use/cover scenarios, Environmental science, Global Flood Risk Assessment and Management, Cover (algebra), Biology, CMIP6, FOS: Environmental engineering, Geomorphology, FOS: Earth and related environmental sciences, 15. Life on land, Geotechnical engineering, climate scenarios, 13. Climate action, FOS: Biological sciences, Environmental Science, Land use, Baro River Basin

Zugangs-URL: https://doaj.org/article/5c832b76e9e245398f43d3bfe0c708ed

Performance analysis of physically-based (HEC-RAS, CADDIES) and AI-based (LSTM) flood models for two case studies

Autoren: Marina Batalini de Macedo Nikunj K. Mangukiya Maria Clara Fava et al.

Quelle: Proceedings of the International Association of Hydrological Sciences, Vol 386, Pp 41-46 (2024)

Schlagwörter: Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, 0208 environmental biotechnology, Urban Flooding, 0207 environmental engineering, 02 engineering and technology, Anomaly Detection in High-Dimensional Data, Global Flood Risk Assessment and Management, Artificial Intelligence, Hydrological Modeling using Machine Learning Methods, GE1-350, 14. Life underwater, QE1-996.5, Global and Planetary Change, Geography, FOS: Environmental engineering, 1. No poverty, Groundwater Level Forecasting, Geology, Flood myth, 15. Life on land, Computer science, 6. Clean water, Environmental sciences, Archaeology, 13. Climate action, Computer Science, Physical Sciences, Environmental Science, Flood Inundation Modeling

Dateibeschreibung: application/pdf

Zugangs-URL: https://piahs.copernicus.org/articles/386/41/2024/
https://doaj.org/article/e8afd355961f4555854fe6c08f32cf2c
https://repository.publisso.de/resource/frl:6482483

Hydrodynamic Investigation of Laguna Lake, Philippines for Water Security and Flood Risk Management of Metro Manila

Autoren: Eugene C. Herrera Kensuke Naito

Quelle: Proceedings of the International Association of Hydrological Sciences, Vol 386, Pp 95-100 (2024)

Schlagwörter: Water resources, Environmental Engineering, Hydrological Modeling, Flood risk management, 0208 environmental biotechnology, 0207 environmental engineering, Urban Flooding, Flood Risk, 02 engineering and technology, Environmental science, 12. Responsible consumption, Global Flood Risk Assessment and Management, Hydrological Modeling using Machine Learning Methods, 11. Sustainability, GE1-350, Real-time Water Quality Monitoring and Aquaculture Management, Business, Biology, Water Science and Technology, Environmental planning, QE1-996.5, Global and Planetary Change, Geography, Ecology, FOS: Environmental engineering, Geology, Flood myth, 15. Life on land, Water resource management, 6. Clean water, Environmental sciences, Surface Water Mapping, Risk management, Archaeology, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, 8. Economic growth, Water security, Water Quality Monitoring, Finance

Dateibeschreibung: application/pdf

Zugangs-URL: https://piahs.copernicus.org/articles/386/95/2024/
https://doaj.org/article/b09ae515044e4ed2ba84a9e515b4dd38
https://repository.publisso.de/resource/frl:6481241

SIMULATION OF FLOOD-PRONE AREAS USING MACHINE LEARNING AND GIS TECHNIQUES IN SAMANGAN PROVINCE, AFGHANISTAN

Autoren: Vahid Isazade Abdul Baser Qasimi Pinliang Dong et al.

Quelle: Geodesy and Cartography, Vol 50, Iss 1 (2024)

Schlagwörter: Artificial intelligence, Perceptron artificial neural network, perceptron artificial neural network, Urban Flooding, Samangan, Organic chemistry, Flood Risk, 02 engineering and technology, 01 natural sciences, Elevation (ballistics), Natural hazard, Engineering, Hydrological Modeling using Machine Learning Methods, 11. Sustainability, Digital elevation model, Water Science and Technology, Perceptron, Global and Planetary Change, Geography, Groundwater Level Forecasting, Hydrology (agriculture), Geology, flood, 6. Clean water, Chemistry, Hydrological Modeling and Water Resource Management, Archaeology, digital elevation model, Physical Sciences, Geodesy, Geographic information system, Hazard, Artificial neural network, Cartography, Land cover, Environmental Engineering, 0207 environmental engineering, Structural engineering, Flood, Environmental science, Global Flood Risk Assessment and Management, Meteorology, Multilayer perceptron, Civil engineering, 14. Life underwater, 0105 earth and related environmental sciences, QB275-343, Afghanistan, FOS: Environmental engineering, FOS: Earth and related environmental sciences, Flood myth, 15. Life on land, Watershed Simulation, Computer science, Geotechnical engineering, 13. Climate action, Environmental Science, Land use, Flood Inundation Modeling, FOS: Civil engineering

Dateibeschreibung: color illustrations; Text

Zugangs-URL: https://doaj.org/article/0289343ce2f84e09ae49546a336b0563

ENSO dataset & comparison of deep learning models for ENSO forecasting

Autoren: Snober S. Mir Arbab Masood Ahmad Sadaqat Ur Rehman

Quelle: Earth Science Informatics. 17:2623-2628

Schlagwörter: Atmospheric Science, Artificial intelligence, Environmental Engineering, Time series, Rainfall-Runoff Modeling, Climate Change and Variability Research, El Niño Southern Oscillation, Quantum mechanics, 7. Clean energy, Environmental science, Meteorology, Hydrological Modeling using Machine Learning Methods, Machine learning, 14. Life underwater, Climatology, Global and Planetary Change, Geography, Physics, FOS: Environmental engineering, Groundwater Level Forecasting, Deep learning, Geology, FOS: Earth and related environmental sciences, Numerical Weather Prediction Models, Power (physics), 15. Life on land, ENSO Variability, Computer science, 6. Clean water, Earth and Planetary Sciences, Reliability (semiconductor), 13. Climate action, Environmental Science, Physical Sciences, Forecasting, Climate Modeling

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Autoren: Alaa Hawamdeh Anwar Tarawneh Yousef Sharrab et al.

Quelle: Water Resources Management. 39:6121-6137

Schlagwörter: Artificial neural network, Artificial intelligence, Environmental Engineering, Environmental science, Reliability engineering, Global Flood Risk Assessment and Management, Engineering, Hydrological Modeling using Machine Learning Methods, Hydrological modelling, Hazard analysis, Biology, Water Science and Technology, Climatology, Global and Planetary Change, Geography, Ecology, FOS: Environmental engineering, Hydrology (agriculture), Geology, FOS: Earth and related environmental sciences, Flood myth, 15. Life on land, Computer science, Water resource management, 6. Clean water, Geotechnical engineering, Hydrological Modeling and Water Resource Management, Archaeology, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, Hazard

Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data

Autoren: Özgür Kişi Salim Heddam Kulwinder Singh Parmar et al.

Quelle: Stochastic Environmental Research and Risk Assessment. 38:2489-2519

Schlagwörter: Cartography, Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Drainage basin, Electricity Price and Load Forecasting Methods, 0208 environmental biotechnology, 0207 environmental engineering, Social Sciences, Structural engineering, Multivariable Grey Model, Streamflow, Multivariate adaptive regression splines, 02 engineering and technology, Management Science and Operations Research, Decision Sciences, Engineering, Cluster analysis, Spline (mechanical), Hydrological Modeling using Machine Learning Methods, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, Data mining, Computational intelligence, Application of Grey System Theory in Forecasting, Geography, Statistics, FOS: Environmental engineering, Groundwater Level Forecasting, Computer science, Multivariate statistics, Regression, 13. Climate action, Monthly streamflow prediction, Machine learning, Original Paper, Principal component regression, Remote sensing data, MARS-Kmeans, Environmental Science, Physical Sciences, Polynomial regression, Mathematics, Forecasting Model Optimization, Forecasting

Zugangs-URL: https://repository.publisso.de/resource/frl:6492523

Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index

Autoren: Reza Rezaiy Ani Shabri

Quelle: Water Science and Technology, Vol 89, Iss 3, Pp 745-770 (2024)

Schlagwörter: eemd, sarima, Time Factors, Environmental Engineering, Time series, Rainfall-Runoff Modeling, eemd-arima, 0208 environmental biotechnology, 0207 environmental engineering, Climate Change and Variability Research, 02 engineering and technology, Precipitation, White noise, Environmental technology. Sanitary engineering, Autoregressive model, Hilbert–Huang transform, Meteorology, Hydrological Modeling using Machine Learning Methods, FOS: Mathematics, Series (stratigraphy), Biology, TD1-1066, Global and Planetary Change, Autoregressive integrated moving average, Geography, 4. Education, spi, Statistics, FOS: Environmental engineering, Paleontology, 15. Life on land, Computer science, 6. Clean water, Droughts, Algorithm, World Wide Web, drought forecasting, 13. Climate action, Residual, Global Drought Monitoring and Assessment, Environmental Science, Physical Sciences, Mean squared error, Mean absolute percentage error, arima, Mathematics, Climate Modeling, Forecasting, Index (typography)

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38358500
https://doaj.org/article/5576730144704e119818b249b77816fa

Artificial neural networks for monthly precipitation prediction in north-west Algeria: a case study in the Oranie-Chott-Chergui basin

Autoren: Ahcene Bouach

Quelle: Journal of Water and Climate Change, Vol 15, Iss 2, Pp 582-592 (2024)

Schlagwörter: Artificial neural network, Physical geography, Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Electricity Price and Load Forecasting Methods, 0208 environmental biotechnology, 0207 environmental engineering, hydrology, Precipitation, 02 engineering and technology, algeria, Environmental technology. Sanitary engineering, precipitation prediction, Engineering, Meteorology, Hydrological Modeling using Machine Learning Methods, water management, FOS: Electrical engineering, electronic engineering, information engineering, GE1-350, 14. Life underwater, Electrical and Electronic Engineering, TD1-1066, Climatology, 2. Zero hunger, Global and Planetary Change, Geography, FOS: Environmental engineering, Groundwater Level Forecasting, Geology, Geomorphology, FOS: Earth and related environmental sciences, 15. Life on land, Computer science, Structural basin, 6. Clean water, Environmental sciences, 13. Climate action, Environmental Science, Physical Sciences, Global Drought Monitoring and Assessment, artificial neural network (ann), Short-Term Forecasting, Probabilistic Forecasting, Forecasting

Zugangs-URL: https://doaj.org/article/098daa2a4f8145888b4ea4ff39f176ba

A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir

Autoren: Phuoc Nguyen Trường Huy Nguyễn The Hung Nguyen

Quelle: River, Vol 3, Iss 1, Pp 107-117 (2024)

Schlagwörter: Environmental Engineering, Hydrological Modeling, Rainfall-Runoff Modeling, 0208 environmental biotechnology, 0207 environmental engineering, GC1-1581, 02 engineering and technology, Oceanography, 7. Clean energy, HEC‐HMS, Environmental science, River, lake, and water-supply engineering (General), Global Flood Risk Assessment and Management, Meteorology, Engineering, Hydrological Modeling using Machine Learning Methods, hydrological hybrid model, Krong H'nang, Petroleum engineering, Water Science and Technology, TC401-506, Global and Planetary Change, Flood forecasting, Geography, FOS: Environmental engineering, Groundwater Level Forecasting, Hydrology (agriculture), Geology, FOS: Earth and related environmental sciences, Flood myth, 15. Life on land, Watershed Simulation, Computer science, 6. Clean water, real‐time flood forecasting, Geotechnical engineering, machine learning, Hydrological Modeling and Water Resource Management, Archaeology, 13. Climate action, Electrical engineering, Environmental Science, Physical Sciences, Flood Inundation Modeling, Hydropower

Zugangs-URL: https://doaj.org/article/ac041320223d46d6b11e26dd217d2ede

Evaluation of Hydrometeorological Characteristics in the Northern Ethiopia, Gerado Catchment

Autoren: Amare Gebremedhin Nigusse AbdulAziz Hussien Destalem Niguse et al.

Quelle: Ethiopian Journal of Water Science and Technology. 5:102-141

Schlagwörter: Cartography, 2. Zero hunger, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, Hydrometeorology, Drainage basin, Geography, FOS: Environmental engineering, 1. No poverty, Groundwater Level Forecasting, Precipitation, 15. Life on land, Environmental science, 6. Clean water, Integrated Management of Water, Energy, and Food Resources, Hydrological Modeling and Water Resource Management, Meteorology, Hydrological Modeling using Machine Learning Methods, 13. Climate action, Environmental Science, Physical Sciences, 8. Economic growth, Water Science and Technology

Machine learning, Water Quality Index, and GIS-based analysis of groundwater quality

Autoren: Ghulam Shabir Solangi Zangeneh Asadi Mohammad Ali Muhammad Bilal et al.

Quelle: Water Practice and Technology, Vol 19, Iss 2, Pp 384-400 (2024)

Schlagwörter: Environmental Engineering, wqi, 0207 environmental engineering, Epistemology, 02 engineering and technology, gis, Environmental technology. Sanitary engineering, 01 natural sciences, Environmental science, Hydrological Modeling using Machine Learning Methods, groundwater, Water Quality Index, Real-time Water Quality Monitoring and Aquaculture Management, Groundwater Quality Assessment, 14. Life underwater, Groundwater, Biology, TD1-1066, Water Science and Technology, 0105 earth and related environmental sciences, Assessment of Surface Water Quality, 2. Zero hunger, Principal Component Analysis, Ecology, FOS: Environmental engineering, prediction models, Groundwater Level Forecasting, Hydrology (agriculture), Geology, FOS: Earth and related environmental sciences, 15. Life on land, Computer science, Water resource management, 6. Clean water, FOS: Philosophy, ethics and religion, World Wide Web, Geotechnical engineering, Philosophy, machine learning, Water quality, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, 8. Economic growth, Quality (philosophy), Water Quality Monitoring, Index (typography)

Zugangs-URL: https://doaj.org/article/dc498a6f8fa9468da65ff3b287f25ad4

Reference Evapotranspiration Modeling Using Artificial Intelligence-Based Approaches: A Bibliometric Analysis

Autoren: Ahmed Skhiri Ali Ferhi Anis Bousselmi et al.

Quelle: American Journal of Agricultural Science, Engineering, and Technology. 8:23-32

Schlagwörter: Web of science, China, Artificial intelligence, Environmental Engineering, FOS: Political science, MEDLINE, FOS: Law, Library science, Data science, 12. Responsible consumption, Hydrological Modeling using Machine Learning Methods, 11. Sustainability, Political science, Biology, Water Science and Technology, Global and Planetary Change, Evapotranspiration, Geography, Ecology, Global Forest Drought Response and Climate Change, 9. Industry and infrastructure, 4. Education, FOS: Environmental engineering, 15. Life on land, Computer science, Model Evaluation, Hydrological Modeling and Water Resource Management, Archaeology, Bibliometrics, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, 8. Economic growth, Law

Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation

Autoren: Chenmin Ni Muhammad Fadhil Marsani Fam Pei Shan et al.

Quelle: AIMS Mathematics, Vol 9, Iss 6, Pp 14681-14696 (2024)

Schlagwörter: Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, 0211 other engineering and technologies, Convolutional neural network, 02 engineering and technology, flood prediction, Systems engineering, Global Flood Risk Assessment and Management, Engineering, Hydrological Modeling using Machine Learning Methods, Machine learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, temporal convolutional networks, Water Science and Technology, Global and Planetary Change, Geography, 4. Education, FOS: Environmental engineering, Unit (ring theory), gated recurrent units, Groundwater Level Forecasting, Flood myth, 15. Life on land, Watershed Simulation, Computer science, Mathematics education, 6. Clean water, Hydrological Modeling and Water Resource Management, subtraction-average-based optimizer, Archaeology, 13. Climate action, Environmental Science, Physical Sciences, kernel density estimation, Estimation, Mean squared prediction error, Flood Inundation Modeling, Mathematics, Forecasting

Zugangs-URL: https://doaj.org/article/f8eff84a931d40fbb0883ce291ca6f1f