Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks

AA Nadiri, Z Sedghi, R Khatibi, S Sadeghfam - Journal of environmental …, 2018 - Elsevier
An investigation is presented to improve on the performances of the Basic DRASTIC
Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of …

Machine learning models combined with wavelet transform and phase space reconstruction for groundwater level forecasting

A Wei, X Li, L Yan, Z Wang, X Yu - Computers & Geosciences, 2023 - Elsevier
Reliable forecasting of groundwater levels plays an important role in water resource
management and the prevention of environmental problems. This study aims to introduce a …

A framework for 'Inclusive Multiple Modelling'with critical views on modelling practices–Applications to modelling water levels of Caspian Sea and Lakes Urmia and …

R Khatibi, MA Ghorbani, S Naghshara, H Aydin… - Journal of …, 2020 - Elsevier
A framework is formulated in this paper for data-driven modelling practices to characterise
Inclusive Multiple Modelling (IMM) practices with multiple goals of enhancing the extracted …

Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy–Directions of innovation towards next generation practices

R Khatibi, MA Ghorbani, FA Pourhosseini - Advanced Engineering …, 2017 - Elsevier
Stream flow prediction is studied by Artificial Intelligence (AI) in this paper using Artificial
Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg …

Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT

AA Nadiri, Z Taheri, R Khatibi, G Barzegari… - Science of the Total …, 2018 - Elsevier
Abstract Proof-of-concept (PoC) is presented for a new framework to serve as a proactive
capability to mapping subsidence vulnerability of Shabestar plain of approximately 500 km 2 …

Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability

M Moazamnia, Y Hassanzadeh, AA Nadiri, R Khatibi… - Journal of …, 2019 - Elsevier
A modelling strategy is formulated, which collectively consists of separate Multiple Models
(MM) and uses Bayesian Model Averaging (BMA) to combine these MMs to learn from data …

Nonlinear and periodic dynamics of chaotic hydro-thermal process of Skokomish river

H Ruskeepää, LN Ferreira, MA Ghorbani… - … Research and Risk …, 2023 - Springer
This paper investigates the dynamics of the time-series of water temperature of the
Skokomish River (2019–2020) at hourly time scale by employing well-known nonlinear …

A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction

H Delafrouz, A Ghaheri, MA Ghorbani - Soft Computing, 2018 - Springer
The main purpose of this study is to construct a new hybrid model (PSR–ANN) by combining
phase space reconstruction (PSR) and artificial neural network (ANN) techniques to raise …

Inter-comparison of time series models of lake levels predicted by several modeling strategies

R Khatibi, MA Ghorbani, L Naghipour… - Journal of …, 2014 - Elsevier
Five modeling strategies are employed to analyze water level time series of six lakes with
different physical characteristics such as shape, size, altitude and range of variations. The …

Combining deterministic modelling with artificial neural networks for suspended sediment estimates

O Makarynskyy, D Makarynska, M Rayson… - Applied Soft …, 2015 - Elsevier
Estimates of suspended sediment concentrations and transport are an important part of any
marine environment assessment study because these factors have a direct impact on the life …