Stochastic Environmental Research and Risk Assessment

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 2020-10-01 to 2024-10-01.)
ArticleCitations
Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction134
District based flood risk assessment in Istanbul using fuzzy analytical hierarchy process77
Projections of precipitation over China based on CMIP6 models74
Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model69
Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India66
Stream water quality prediction using boosted regression tree and random forest models65
Review of landslide susceptibility assessment based on knowledge mapping63
Renewable energy, economic freedom and economic policy uncertainty: New evidence from a dynamic panel threshold analysis for the G-7 and BRIC countries57
Exposure and health: A progress update by evaluation and scientometric analysis55
A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression54
Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India52
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 s49
Artificial Intelligence models for prediction of the tide level in Venice49
Development of new machine learning model for streamflow prediction: case studies in Pakistan47
A novel hybrid dragonfly optimization algorithm for agricultural drought prediction46
Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India46
Occupational health, safety and environmental risk assessment in textile production industry through a Bayesian BWM-VIKOR approach40
Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions39
Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China39
LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios39
Changes in monthly streamflow in the Hindukush–Karakoram–Himalaya Region of Pakistan using innovative polygon trend analysis37
Developing hybrid time series and artificial intelligence models for estimating air temperatures37
Landslide susceptibility mapping in Three Gorges Reservoir area based on GIS and boosting decision tree model36
Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran36
A new soft computing model for daily streamflow forecasting35
A new principal component analysis by particle swarm optimization with an environmental application for data science35
A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment33
Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble31
Markov chain Monte Carlo with neural network surrogates: application to contaminant source identification31
Trivariate joint probability model of typhoon-induced wind, wave and their time lag based on the numerical simulation of historical typhoons30
Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model30
Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model30
Hybrid deep learning method for a week-ahead evapotranspiration forecasting30
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