M
Mohammad Taghi Aalami
Researcher at University of Tabriz
Publications - 32
Citations - 738
Mohammad Taghi Aalami is an academic researcher from University of Tabriz. The author has contributed to research in topics: Environmental science & Precipitation. The author has an hindex of 10, co-authored 25 publications receiving 421 citations. Previous affiliations of Mohammad Taghi Aalami include Hong Kong Polytechnic University.
Papers
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Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
TL;DR: In this paper, a coupled CNN-LSTM model was proposed to predict water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L), in the Small Prespa Lake in Greece.
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Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River
Bahare Raheli,Mohammad Taghi Aalami,Ahmed El-Shafie,Mohammad Ali Ghorbani,Mohammad Ali Ghorbani,Ravinesh C. Deo +5 more
TL;DR: In this article, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia.
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Evaluation of total load sediment transport formulas using ANN
TL;DR: In this paper, an Artificial Neural Network (ANN) model is trained using four dominant parameters of sediment transport formulas to estimate total bed material load, including average flow velocity, water surface slopes, average flow depth, and median particle diameter.
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Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting
TL;DR: In this paper, a hybrid CNN-LSTM model was proposed to predict lake water level in Lake Michigan and Lake Ontario by coupling boundary corrected (BC) Maximal Overlap Discrete Wavelet Transform (MODWT) data preprocessing with a hybrid Convolutional Neural Network (CNN) Long Short Term Memory (LSTMs) deep learning (DL) model.
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Trend analysis of groundwater using non-parametric methods (case study: Ardabil plain)
TL;DR: In this paper, the trends in groundwater level and fifteen hydro-geochemical elements at 32 piezometric stations located in the Ardabil plain of the northwest of Iran were analyzed using the nonparametric Mann-Kendall method after removing the effect of significant lag-1 serial correlation from the respective time series by pre-whitening.