Adrienn Dineva
Óbuda University
57 Papers
62 Citations
Adrienn Dineva is an academic researcher from Óbuda University. The author has contributed to research in topics: Adaptive control & Lyapunov function. The author has an hindex of 13, co-authored 47 publications. Previous affiliations of Adrienn Dineva include Selye János University & Duy Tan University.
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Papers
Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
Amirhosein Mosavi,Farzaneh Sajedi Hosseini,Bahram Choubin,Massoud Goodarzi,Adrienn Dineva,Elham Rafiei Sardooi +5 more
TL;DR: Groundwater potential maps predicted in this study can help water resources managers and policymakers in the fields of watershed and aquifer management to preserve an optimal exploit from this important freshwater.
216
Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models
Amirhosein Mosavi,Farzaneh Sajedi-Hosseini,Bahram Choubin,Fereshteh Taromideh,Gholamreza Rahi,Adrienn Dineva +5 more
TL;DR: In this article, the authors proposed novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil, including weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) methods.
Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins
Amirhosein Mosavi,Mohammad Golshan,Saeid Janizadeh,Bahram Choubin,Assefa M. Melesse,Adrienn Dineva +5 more
TL;DR: The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change and human interventions as discussed by the authors, and hazard mapping is essential for local policymaking. But it is difficult to estimate the hazard of watersheds.
112
Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models
Amirhosein Mosavi,Farzaneh Sajedi Hosseini,Bahram Choubin,Mahsa Abdolshahnejad,Hamidreza Gharechaee,Ahmadreza Lahijanzadeh,Adrienn Dineva +6 more
TL;DR: In this article, the performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA).
83
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping
Amirhosein Mosavi,Ataollah Shirzadi,Bahram Choubin,Fereshteh Taromideh,Farzaneh Sajedi Hosseini,Moslem Borji,Himan Shahabi,Aryan Salvati,Adrienn Dineva +8 more
TL;DR: The main aim of this study is to introduce and implement an ensemble machine learning model of random subspace (RS) based on a classifier, functional tree (FT), named RSFT model for snow avalanche susceptibility mapping at Karaj Watershed, Iran.