OOIR: Observatory of International Research

Papers

(The H4-Index of Stochastic Environmental Research and Risk Assessment is 30. 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 2020-09-01 to 2024-09-01.)

Article	Citations
Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction	133
Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms	131
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction	99
District based flood risk assessment in Istanbul using fuzzy analytical hierarchy process	77
Projections of precipitation over China based on CMIP6 models	74
Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model	69
Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India	66
Stream water quality prediction using boosted regression tree and random forest models	64
Review of landslide susceptibility assessment based on knowledge mapping	63
Exposure and health: A progress update by evaluation and scientometric analysis	55
A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression	54
Renewable energy, economic freedom and economic policy uncertainty: New evidence from a dynamic panel threshold analysis for the G-7 and BRIC countries	53
Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India	51
A probabilistic-deterministic analysis of human health risk related to the exposure to potentially toxic elements in groundwater of Urmia coastal aquifer (NW of Iran) with a special focus on arsenic s	49
Artificial Intelligence models for prediction of the tide level in Venice	48
Development of new machine learning model for streamflow prediction: case studies in Pakistan	46
Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India	45
A novel hybrid dragonfly optimization algorithm for agricultural drought prediction	44
LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios	39
Occupational health, safety and environmental risk assessment in textile production industry through a Bayesian BWM-VIKOR approach	38
Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions	38
Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China	37
Changes in monthly streamflow in the Hindukush–Karakoram–Himalaya Region of Pakistan using innovative polygon trend analysis	37
Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran	36
Developing hybrid time series and artificial intelligence models for estimating air temperatures	36

Landslide susceptibility mapping in Three Gorges Reservoir area based on GIS and boosting decision tree model	35
A new soft computing model for daily streamflow forecasting	35
A new principal component analysis by particle swarm optimization with an environmental application for data science	34
A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment	32
Markov chain Monte Carlo with neural network surrogates: application to contaminant source identification	31
Trivariate joint probability model of typhoon-induced wind, wave and their time lag based on the numerical simulation of historical typhoons	30
Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble	30
Hybrid deep learning method for a week-ahead evapotranspiration forecasting	30
Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model	30