OOIR: Observatory of International Research

Papers

(The H4-Index of Environmental Modelling & Software is 35. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-02-01 to 2025-02-01.)

Article	Citations
Nitrogen prediction in the Great Barrier Reef using finite element analysis with deep neural networks	297
Development of an integrated modeling platform for watershed simulation	147
Synthetic random environmental time series generation with similarity control, preserving original signal’s statistical characteristics	104
Facilitating open data and open model integration with generic parameter input file generators in the CyberWater framework	99
AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing	88
An open framework for analysing future flood risk in urban areas	84
An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty	80
Solving the Master Equation on river networks: A computer algebra approach	66
A coupled multiscale description of seasonal Physical–BioGeoChemical dynamics in Southern Ocean Marginal Ice Zone	63
A process-based framework for validating forest landscape modeling outcomes	61
PolarBytes: Advancing polar research with a centralized open-source data sharing platform	56
Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework	56
Conditional interval reduction method: A possible new direction for the optimization of process based models	56
PyCHAMP: A crop-hydrological-agent modeling platform for groundwater management	55
Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas	54
Transfer learning with convolutional neural networks for hydrological streamline delineation	54
Enhancing algal bloom forecasting: A novel framework for machine learning performance evaluation during periods of special temporal patterns	53
Editorial Board	53
Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km51
Autoethnographic assessment of a manifesto for more trustworthy, relevant, and just models	47
A systematic literature review on lake water level prediction models	45
Topical 3D modelling and simulation of air dispersion hazards as a new paradigm to support emergency preparedness and response	44
Assessing physical and biological lake oxygen indicators using simulated environmental variables and machine learning algorithms	43
Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model	43
Heavy: Software for forward modeling gravity change from MODFLOW output	42

An automatic partition-based parallel algorithm for grid-based distributed hydrological models	40
Introductory overview: Systems and control methods for operational management support in agricultural production systems	39
An open source cyberinfrastructure for collecting, processing, storing and accessing high temporal resolution residential water use data	38
The Danish Lagrangian Model (DALM): Development of a new local-scale high-resolution air pollution model	37
Spatiotemporal prediction of carbon emissions using a hybrid deep learning model considering temporal and spatial correlations	37
HyPix: 1D physically based hydrological model with novel adaptive time-stepping management and smoothing dynamic criterion for controlling Newton–Raphson step	37
Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States	36
Transfer learning in environmental data-driven models: A study of ozone forecast in the Alpine region	36
Quantifying phenological asynchrony of phyto- and zooplankton in response to changing temperature and nutrient conditions in Lake Müggelsee (Germany) by means of evolutionary computation	35
Automatic classification of land cover from LUCAS in-situ landscape photos using semantic segmentation and a Random Forest model	35