OOIR: Observatory of International Research

Papers

(The H4-Index of Stochastic Environmental Research and Risk Assessment is 29. 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 2021-06-01 to 2025-06-01.)

Article	Citations
Modelling multidecadal variability in flood frequency using the Two-Component Extreme Value distribution	111
WaveTransTimesNet: an enhanced deep learning monthly runoff prediction model based on wavelet transform and transformer architecture	90
Empirical mode decomposition for improved radar wind estimation during rainy conditions	83
A new interpretable prediction framework for step-like landslide displacement	75
Application of uncertain hurricane climate change projections to catastrophe risk models	66
Dispersivity variations of solute transport in heterogeneous sediments: numerical and experimental study	62
Multivariate stochastic Vasicek diffusion process: computational estimation and application to the analysis of $$CO_2$$ and $$N_2O$$ concentrations	60
Modeling and predicting mean indoor radon concentrations in Austria by generalized additive mixed models	57
Assessment of loss of life caused by dam failure based on fuzzy theory and hybrid random forest model	53
Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios	48
A conditional machine learning classification approach for spatio-temporal risk assessment of crime data	48
Quantifying the weekly cycle effect of air pollution in cities of China	46
Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq	45
Novel MCDA methods for flood hazard mapping: a case study in Hamadan, Iran	45
Simulation of earthquake ground motion via stochastic finite-fault modeling considering the effect of rupture velocity	43
Determining the percentile threshold of daily extreme precipitation, methods evaluation	41
A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases	41
Assessing climate change risks using multi-criteria decision-making (MCDM) techniques in Raichur Taluk, Karnataka, India	40
Hydrometeorological-modeling-based analysis and risk assessment of a torrential rainfall flash flood in a data deficient area in Wenchuan County, Sichuan Province, China	39
Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction	39
Evaluating extreme precipitation in gridded datasets with a novel station database in Morocco	39
Developing an earthquake damaged-based multi-severity casualty method by using Monte Carlo simulation and fuzzy logic; case study: Mosha fault seismic scenario, Tehran, Iran	36
Master equation model for solute transport in river basins: part I channel fluvial scale	36
Addressing the relevance of COVID–19 pandemic in nature and human socio-economic fate	36
Predictive modeling of water quality index (WQI) classes in Indian rivers: Insights from the application of multiple Machine Learning (ML) models on a decennial dataset	34

Investigating seasonal drought severity-area-frequency (SAF) curve over Indian region: incorporating GCM and scenario uncertainties	33
A new promoted Surface Water Supply Index for multi-faceted drought assessment	32
Inferring causal associations in hydrological systems: a comparison of methods	31
Predicting saturated hydraulic conductivity from particle size distributions using machine learning	29
Spatiotemporal data science: theoretical advances and applications	29
A Bayesian spatio-temporal model for cluster detection: identifying HPV suboptimal vaccine coverage	29
River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India	29
Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique	29