Search Results - Hydrological Modeling using Machine Learning Methods

Water quality prediction using machine learning models based on grid search method

Authors: Mahmoud Y. Shams Ahmed M. Elshewey El-Sayed M. El-kenawy et al.

Source: Multimedia Tools and Applications. 83:35307-35334

Subject Terms: Artificial neural network, Artificial intelligence, Environmental Engineering, Support vector machine, Rainfall-Runoff Modeling, Extreme learning machine, Missing data, Overfitting, 02 engineering and technology, Boosting (machine learning), 01 natural sciences, Hydrological Modeling using Machine Learning Methods, Ensemble learning, Water Quality Index, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Decision tree, FOS: Mathematics, Multilayer perceptron, Real-time Water Quality Monitoring and Aquaculture Management, Groundwater Quality Assessment, 14. Life underwater, Imputation (statistics), Hyperparameter optimization, 0105 earth and related environmental sciences, Water Science and Technology, Assessment of Surface Water Quality, Perceptron, 4. Education, AdaBoost, Statistics, FOS: Environmental engineering, Groundwater Level Forecasting, Ensemble forecasting, 15. Life on land, Computer science, Regression, 6. Clean water, 13. Climate action, Environmental Science, Physical Sciences, Gradient boosting, Water Quality Monitoring, Mathematics, Random forest

Run-off modelling of pervious and impervious areas using couple SWAT and a novel machine learning model in cross-rivers state Nigeria

Authors: Badiya Salele Yakubu Aminu Dodo Dalhatu Aliyu Sani et al.

Source: Water Science and Technology, Vol 88, Iss 7, Pp 1893-1909 (2023)

Subject Terms: Urban Flooding, Precipitation, Environmental technology. Sanitary engineering, sdg 13, remote sensing, Soil, Engineering, Hydrological Modeling using Machine Learning Methods, 11. Sustainability, Psychology, Runoff curve number, TD1-1066, Global and Planetary Change, Geography, Ecology, Groundwater Level Forecasting, Soil and Water Assessment Tool, Hydrology (agriculture), flood, Surface runoff, 6. Clean water, FOS: Psychology, machine learning, Physical Sciences, Cartography, Environmental Engineering, Rainfall-Runoff Modeling, Drainage basin, Urban Stormwater Management and Sustainable Drainage Systems, Nigeria, Streamflow, gis, Flooding (psychology), Environmental science, Global Flood Risk Assessment and Management, Meteorology, Water Movements, Rainwater Harvesting, Biology, Impervious surface, FOS: Environmental engineering, Water, 15. Life on land, Floods, Geotechnical engineering, 13. Climate action, FOS: Biological sciences, Environmental Science, Psychotherapist, soil water assessment tool, Flood Inundation Modeling

Access URL: https://pubmed.ncbi.nlm.nih.gov/37831003
https://doaj.org/article/3801fcb6ebc648e29d009b9ae1875595

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

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

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

Subject Terms: 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

File Description: color illustrations; Text

Access URL: https://doaj.org/article/0289343ce2f84e09ae49546a336b0563

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Authors: Huu Duy Nguyen

Source: Journal of Water and Climate Change, Vol 14, Iss 1, Pp 200-222 (2023)

Subject Terms: Cartography, Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, Adaptive neuro fuzzy inference system, Environmental technology. Sanitary engineering, Flooding (psychology), Estimator, 01 natural sciences, Global Flood Risk Assessment and Management, ha tinh, Hydrological Modeling using Machine Learning Methods, Machine learning, FOS: Mathematics, Psychology, GE1-350, 14. Life underwater, Data mining, TD1-1066, 0105 earth and related environmental sciences, Global and Planetary Change, Geography, Statistics, adaptive neuro-fuzzy inference system, FOS: Environmental engineering, Groundwater Level Forecasting, flood, Flood myth, 15. Life on land, vietnam, Computer science, 6. Clean water, Environmental sciences, Surface Water Mapping, Fuzzy logic, FOS: Psychology, Fuzzy control system, Archaeology, 13. Climate action, Environmental Science, Physical Sciences, Global Drought Monitoring and Assessment, Psychotherapist, Mean squared error, Flood Inundation Modeling, Geographic information system, Mathematics

Access URL: https://doaj.org/article/e91e0379c83f4ba5bda8fd33c2ed6ee3

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

Authors: Ahmed Skhiri Ali Ferhi Anis Bousselmi et al.

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

Subject Terms: 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

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

Authors: Phuoc Nguyen Trường Huy Nguyễn The Hung Nguyen

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

Subject Terms: 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

Access URL: https://doaj.org/article/ac041320223d46d6b11e26dd217d2ede

Assessment of Groundwater Potential in a Mountainous Area Using Machine Learning and GIS, Rherhaya Basin, High Atlas, Morocco

Authors: Mourad Jadoud Abderrahim El Achheb Nour‐Eddine Laftouhi et al.

Source: Iraqi Geological Journal, Vol 56, Iss 2D, Pp 288-306 (2023)

Subject Terms: Cartography, Environmental Engineering, Support vector machine, Hydrological Modeling, Hydrogeology, Environmental science, Groundwater recharge, Global Flood Risk Assessment and Management, Hydrological Modeling using Machine Learning Methods, Machine learning, 14. Life underwater, Groundwater, Terrain, 2. Zero hunger, QE1-996.5, Global and Planetary Change, Geography, Groundwater Potential, Groundwater flow, FOS: Environmental engineering, Groundwater Level Forecasting, Hydrology (agriculture), Geology, Geomorphology, FOS: Earth and related environmental sciences, 15. Life on land, Watershed, Computer science, Mapping Groundwater Potential Zones Using GIS Techniques, Structural basin, 6. Clean water, Surface Water Mapping, Hydrological Analysis, Geotechnical engineering, Groundwater model, 13. Climate action, Aquifer properties, Environmental Science, Physical Sciences, Aquifer

Access URL: https://doaj.org/article/0790267bb1fb469584aedda9afcf023b

Modeling the Semivariogram of Climatic Scenario around Rivers by Using Stream Network Mapping and Hydrological Indicator

Authors: L. Omolayo Agashua B. Dorcas Oluyemi-Ayibiowu N. Isioma Ihimekpen et al.

Source: Journal of Human, Earth, and Future, Vol 3, Iss 1, Pp 17-31 (2022)

Subject Terms: Precipitation, Spatial variability, 01 natural sciences, Context (archaeology), Hydrological Modeling using Machine Learning Methods, Meteorology. Climatology, Pathology, Water Science and Technology, Global and Planetary Change, Geography, Ecology, impact flow, Statistics, Groundwater Level Forecasting, Hydrology (agriculture), Geology, Remote sensing, geostatistical, 6. Clean water, Surface Water Mapping, Kriging, Hydrological Modeling and Water Resource Management, Archaeology, Human ecology. Anthropogeography, river, Physical Sciences, Medicine, Vegetation (pathology), Physical geography, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, spatial reliance, contagions, Environmental science, Global Flood Risk Assessment and Management, GF1-900, Meteorology, FOS: Mathematics, Spatial ecology, Variogram, Spatial distribution, 14. Life underwater, Biology, 0105 earth and related environmental sciences, FOS: Environmental engineering, distribution mapping, FOS: Earth and related environmental sciences, 15. Life on land, Geotechnical engineering, 13. Climate action, FOS: Biological sciences, Environmental Science, hydrological indicator, QC851-999, Mediterranean climate, Mathematics, Forecasting

Access URL: https://doaj.org/article/f05c45f462ad4b62aa3c99ff4fd6f432

Hydrological model parameter regionalization: Runoff estimation using machine learning techniques in the Tha Chin River Basin, Thailand

Authors: Phyo Thandar Hlaing Usa Wannasingha Humphries Muhammad Waqas

Source: MethodsX
MethodsX, Vol 13, Iss, Pp 102792-(2024)

Subject Terms: Cartography, Chin, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, Drainage basin, Science, Environmental science, Systems engineering, Global Flood Risk Assessment and Management, Engineering, Hydrological Modeling using Machine Learning Methods, Biology, Water Science and Technology, Global and Planetary Change, Geography, Ecology, FOS: Environmental engineering, Groundwater Level Forecasting, Paleontology, Hydrology (agriculture), Geology, Geomorphology, FOS: Earth and related environmental sciences, Advancements in Daily Precipitation Prediction, 15. Life on land, Watershed Simulation, Surface runoff, Structural basin, 6. Clean water, Geotechnical engineering, Hydrological Modeling and Water Resource Management, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, Estimation, Flood Inundation Modeling

Access URL: https://pubmed.ncbi.nlm.nih.gov/39022181
https://doaj.org/article/9594f46ab56e4d9187072411eae92895

Monthly streamflow forecasting for the Hunza River Basin using machine learning techniques

Authors: Sunaid Khan Mehran Khan Afed Ullah Khan et al.

Source: Water Practice and Technology, Vol 18, Iss 8, Pp 1959-1969 (2023)

Subject Terms: 0208 environmental biotechnology, 02 engineering and technology, Precipitation, Boosting (machine learning), gradient boosting, Environmental technology. Sanitary engineering, 7. Clean energy, Hydrological Modeling using Machine Learning Methods, TD1-1066, Streamflow Trends, Water Science and Technology, Global and Planetary Change, Geography, Statistics, Groundwater Level Forecasting, Hydrology (agriculture), Geology, 6. Clean water, machine learning, Hydrological Modeling and Water Resource Management, Physical Sciences, Gradient boosting, streamflow, adaptive boosting, Cartography, Environmental Engineering, Rainfall-Runoff Modeling, Drainage basin, 0207 environmental engineering, forecasting, Streamflow, Environmental science, Global Flood Risk Assessment and Management, Meteorology, Machine learning, FOS: Mathematics, 14. Life underwater, knn, FOS: Environmental engineering, FOS: Earth and related environmental sciences, 15. Life on land, Computer science, Geotechnical engineering, 13. Climate action, Environmental Science, Mean squared error, random forest, Flood Inundation Modeling, Mathematics, Forecasting, Random forest

Access URL: https://doaj.org/article/b187ed2957484ece814ddb2fcbe2f28a

Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree

Authors: Salim Heddam Nguyen-Thoi Trung Mohammad Zounemat-Kermani et al.

Source: Theoretical and Applied Climatology. 145:639-659

Subject Terms: Artificial neural network, Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Ecosystem Resilience, 0208 environmental biotechnology, 0207 environmental engineering, Optimal Operation of Water Resources Systems, Ocean Engineering, 02 engineering and technology, Mathematical analysis, 01 natural sciences, Wind speed, Engineering, Meteorology, Hydrological Modeling using Machine Learning Methods, FOS: Mathematics, Multilayer perceptron, Hydro-Economic Models, Data mining, Biology, 0105 earth and related environmental sciences, Perceptron, 2. Zero hunger, Global and Planetary Change, Evapotranspiration, Radial basis function, Geography, Ecology, Global Forest Drought Response and Climate Change, Statistics, FOS: Environmental engineering, 15. Life on land, Computer science, Regression, 6. Clean water, Model Performance, 13. Climate action, FOS: Biological sciences, Environmental Science, Physical Sciences, Tree (set theory), Regression analysis, Mathematics

Access URL: https://www.researchsquare.com/article/rs-208205/v1.pdf?c=1613705481000
https://www.researchsquare.com/article/rs-208205/v1.pdf?c=1613705481000
https://link.springer.com/10.1007/s00704-021-03645-6
https://link.springer.com/article/10.1007/s00704-021-03645-6
https://www.researchsquare.com/article/rs-208205/v1
https://ui.adsabs.harvard.edu/abs/2021ThApC.145..639K/abstract

Novel Hybrid Machine Learning Algorithms for Lakes Evaporation and Power Production using Floating Semitransparent Polymer Solar Cells

Authors: Ismail Abd‐Elaty Nand Lal Kushwaha Abhishek Patel

Source: Water Resources Management. 37:4639-4661

Subject Terms: Environmental Engineering, Support vector machine, Rainfall-Runoff Modeling, Evaporation, 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, 7. Clean energy, Environmental science, Meteorology, Engineering, Artificial Intelligence, Hydrological Modeling using Machine Learning Methods, Support Vector Machines, Machine learning, 11. Sustainability, FOS: Mathematics, Machine Learning Methods for Solar Radiation Forecasting, 0105 earth and related environmental sciences, Energy, Water Evaporation, Renewable Energy, Sustainability and the Environment, Physics, FOS: Environmental engineering, Groundwater Level Forecasting, Hydrology (agriculture), 15. Life on land, RSS, Computer science, 6. Clean water, Algorithm, Geotechnical engineering, Operating system, 13. Climate action, Atmospheric Water Harvesting, Physical Sciences, Computer Science, Environmental Science, Solar-Powered Water Desalination Technologies, Mathematics

Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models

Authors: Baseer Ullah Muhammad Fawad Afed Ullah Khan et al.

Source: Water Resources Management. 37:6089-6106

Subject Terms: Cartography, Environmental Engineering, Support vector machine, Rainfall-Runoff Modeling, Drainage basin, 0207 environmental engineering, Streamflow, 02 engineering and technology, 01 natural sciences, Environmental science, Global Flood Risk Assessment and Management, Hydrological Modeling using Machine Learning Methods, Machine learning, FOS: Mathematics, Streamflow Trends, Water Science and Technology, 0105 earth and related environmental sciences, Global and Planetary Change, Flood forecasting, Geography, AdaBoost, Statistics, FOS: Environmental engineering, Groundwater Level Forecasting, Flood myth, Watershed Simulation, Computer science, 6. Clean water, Hydrological Modeling and Water Resource Management, Archaeology, 13. Climate action, Environmental Science, Physical Sciences, Mean squared error, Flood Inundation Modeling, Mathematics

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

Authors: Rana Muhammad Adnan Hongliang Dai Özgür Kişi et al.

Source: Environ Sci Pollut Res Int

Subject Terms: Artificial intelligence, Environmental Engineering, Chemical Oxygen Demand, On-line Monitoring of Wastewater Quality, Environmental engineering, 02 engineering and technology, Adaptive neuro fuzzy inference system, Wastewater, Fish Behavior Analysis, 01 natural sciences, Industrial and Manufacturing Engineering, Environmental science, Fuzzy Logic, Hydrological Modeling using Machine Learning Methods, Water Quality, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Real-time Water Quality Monitoring and Aquaculture Management, Total suspended solids, 14. Life underwater, Biology, Water Science and Technology, 0105 earth and related environmental sciences, Biological Oxygen Demand Analysis, Ecology, Particle swarm optimization, Biochemical oxygen demand, Statistics, FOS: Environmental engineering, Coefficient of determination, Computer science, 6. Clean water, Oxygen, Algorithm, Fuzzy logic, Water quality, Fuzzy control system, 13. Climate action, Biological system, FOS: Biological sciences, Environmental Science, Physical Sciences, Mean squared error, Chemical oxygen demand, Water Quality Monitoring, Algorithms, Mathematics, Research Article

Access URL: https://pubmed.ncbi.nlm.nih.gov/37531049

Groundwater level prediction using machine learning models: A comprehensive review

Authors: Hai Tao Mohammed Majeed Hameed Haydar Abdulameer Marhoon et al.

Source: Tao, H, Hameed, M M, Marhoon, H A, Zounemat-Kermani, M, Heddam, S, Sungwon, K, Sulaiman, S O, Tan, M L, Sa'adi, Z, Mehr, A D, Allawi, M F, Abba, S I, Zain, J M, Falah, M W, Jamei, M, Bokde, N D, Bayatvarkeshi, M, Al-Mukhtar, M, Bhagat, S K, Tiyasha, T, Khedher, K M, Al-Ansari, N, Shahid, S & Yaseen, Z M 2022, 'Groundwater level prediction using machine learning models : A comprehensive review', Neurocomputing, vol. 489, pp. 271-308. https://doi.org/10.1016/j.neucom.2022.03.014

Subject Terms: Artificial intelligence, Water resources, Scale (ratio), Resource (disambiguation), 0208 environmental biotechnology, TA Engineering (General). Civil engineering (General), 02 engineering and technology, Data science, Engineering, Hydrological Modeling using Machine Learning Methods, State-of-the-art, Geoteknik och teknisk geologi, Groundwater, Water Science and Technology, Global and Planetary Change, Computer network, Ecology, Computer Sciences, Physics, Groundwater Level Forecasting, 6. Clean water, Surface Water Mapping, Hydrological Modeling and Water Resource Management, Physical Sciences, Environmental Engineering, Hydrological Modeling, Rainfall-Runoff Modeling, 0207 environmental engineering, Mathematical analysis, Quantum mechanics, Global Flood Risk Assessment and Management, Field (mathematics), Machine learning, FOS: Mathematics, 14. Life underwater, Groundwater resources, Data mining, Biology, Domain (mathematical analysis), Input parameters, FOS: Environmental engineering, Predictive modelling, Pure mathematics, Catchment sustainability, 15. Life on land, Geotechnical Engineering and Engineering Geology, Computer science, Geotechnical engineering, Datavetenskap (datalogi), 13. Climate action, FOS: Biological sciences, Environmental Science, Prediction performance, Groundwater level, Aquifer, Flood Inundation Modeling, Mathematics

File Description: application/pdf

Access URL: https://pure.au.dk/portal/en/publications/1f04bc64-1f54-4716-be2d-ee372c6bb500
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-89826
https://pure.au.dk/portal/en/publications/1f04bc64-1f54-4716-be2d-ee372c6bb500
https://www.sciencedirect.com/science/article/pii/S092523122200282X?via=ihub
https://pure.au.dk/ws/files/333407686/1_s2.0_S092523122200282X_main.pdf
https://doi.org/10.1016/j.neucom.2022.03.014

APPLICATION OF GEOSPATIAL AND HYDROLOGICAL MODELING FOR RUNOFF ESTIMATION IN LEBIR RIVER BASIN, MALAYSIA

Authors: S. Mohamad Sukairi S. Harun K. A. Mohd Nasir et al.

Source: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-4-W6-2022, Pp 231-235 (2023)

Subject Terms: Technology, 0211 other engineering and technologies, Precipitation, 02 engineering and technology, 01 natural sciences, Hydrological Modeling using Machine Learning Methods, Psychology, Applied optics. Photonics, Water Science and Technology, Climatology, Global and Planetary Change, Geography, Ecology, Statistics, Groundwater Level Forecasting, Hydrology (agriculture), Geology, Engineering (General). Civil engineering (General), Surface runoff, 6. Clean water, FOS: Psychology, Hydrological Modeling and Water Resource Management, Archaeology, Physical Sciences, Calibration, TA1-2040, Cartography, Environmental Engineering, Geospatial analysis, Rainfall-Runoff Modeling, Hydrological Modeling, Drainage basin, Hydrograph, HEC-HMS, Flooding (psychology), Environmental science, Global Flood Risk Assessment and Management, Meteorology, Hydrological modelling, FOS: Mathematics, Biology, 0105 earth and related environmental sciences, Flood forecasting, FOS: Environmental engineering, FOS: Earth and related environmental sciences, Flood myth, Watershed Simulation, TA1501-1820, Geotechnical engineering, 13. Climate action, FOS: Biological sciences, Environmental Science, Psychotherapist, Flood Inundation Modeling, Mathematics

File Description: application/pdf

Access URL: https://isprs-archives.copernicus.org/articles/XLVIII-4-W6-2022/231/2023/
https://doaj.org/article/9590f7f860ed48f69fdd415e1974335a

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

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

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

Subject Terms: 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

Exploring the Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models

Authors: Bingbing Ding Xinxiao Yu Guodong Jia

Source: Water Resources Research, Vol 61, Iss 5, Pp n/a-n/a (2025)

Subject Terms: streamflow variability, climate change, land use change, machine learning, hydrological modeling, Environmental sciences, GE1-350

File Description: electronic resource

Relation: https://doaj.org/toc/0043-1397; https://doaj.org/toc/1944-7973

Access URL: https://doaj.org/article/a92161d87c3a4de6b5b2af7efc5f6440

Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study

Authors: Alvin Lal Ashneel Sharan Krishneel K. Sharma et al.

Contributors: Alvin Lal Ashneel Sharan Krishneel K. Sharma et al.

Source: Environ Monit Assess

Subject Terms: Salinity, 0208 environmental biotechnology, Sustainable Development Goals, 02 engineering and technology, FEMWATER, Engineering, Hydrological Modeling using Machine Learning Methods, SDG 6, Groundwater, SDG 3, 2. Zero hunger, Physics, Statistics, Groundwater Level Forecasting, Power (physics), 6. Clean water, 3. Good health, group method of data handling (GMDH), machine learning, Reliability (semiconductor), Physical Sciences, Environmental Monitoring, data partitioning strategies, Environmental Engineering, Rainfall-Runoff Modeling, groundwater salinity, 0207 environmental engineering, Quantum mechanics, Environmental science, Artificial Intelligence, FOS: Mathematics, 14. Life underwater, Inductive Modeling in Scientific Research, Data mining, Research, FOS: Environmental engineering, Predictive modelling, Reproducibility of Results, Coefficient of determination, Models, Theoretical, 15. Life on land, Computer science, Geotechnical engineering, 13. Climate action, Groundwater Flow and Transport Modeling, Environmental Science, Computer Science, Mean squared error, Aquifer, Water Pollutants, Chemical, Mathematics

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/38880864

Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China

Authors: Wang, Yuan Shi, Xiaodan Oguchi, Takashi

Source: ISPRS International Journal of Geo-Information. 12(6):238-238

Subject Terms: archaeological predictive modeling, GIS, spatial analysis, deep learning, conditional attention mechanism, frequency ratio model, maximum entropytopographic factors

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-63870
https://doi.org/10.3390/ijgi12060238