Yazar "Albaji, Mohammad" seçeneğine göre listele

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    Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature
    (Springer Science and Business Media B.V., 2024) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad; Albaji, Mohammad
    The precise predicting of air temperature has a significant influence in many sectors such as agriculture, industry, modeling environmental processes. In this work, to predict the mean daily time series air temperature in Mu & gbreve;la city (AT(m)), Turkey, initially, five different layer structures of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning-based neural network models through the seq2seq regression forecast module are developed. Then, based on performance evaluation metrics, an optimal DL-based layer network structure designed is chosen to hybridize with the wavelet transform (WT) algorithm (i.e., WT-DNN model) to enhance the estimation capability. In this direction, among potential meteorological variables considered, the average daily sunshine duration (SSD) (hours), total global solar radiation (TGSR) (kw. hour/m(2)), and total global insolation intensity (TGSI) (watt/m(2)) from Jan 2014 to Dec 2019 are picked as the most effective input variables through correlation analysis to predict AT(m). To thwart overfitting and underfitting problems, different algorithm tuning along with trial-and-error procedures through diverse types of hyper-parameters are performed. Consistent with the performance evaluation standards, comparison plots, and Total Learnable Parameters (TLP) value, the state-of-the-art and unique proposed hybrid WT-(LSTM x GRU) model (i.e., hybrid WT with the coupled version of LSTM and GRU models via Multiplication layer (x)) is confirmed as the best model developed. This hybrid model under the ideal hyper-parameters resulted in an R-2 = 0.94, an RMSE = 0.56 (degrees C), an MBE = -0.5 (degrees C), AICc = -382.01, and a running time of 376 (s) in 2000 iterations. Nonetheless, the standard single LSTM layer network model as benchmark model resulted in an R-2 = 0.63, an RMSE = 4.69 (degrees C), an MBE = -0.89 (degrees C), AICc = 1021.8, and a running time of 186 (s) in 2000 iterations.
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    Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks
    (Elsevier, 2022) Gharehbaghi, Amin; Ghasemlounia, Redvan; Ahmadi, Farshad; Albaji, Mohammad
    Precise estimation of groundwater level (GWL) fluctuations has a substantial effect on water resources management. In the present study, to forecast the regional mean monthly time series groundwater level (GWL) with a range of 4.82 (m) in Urmia plain, three different layer structures of Gated Recurrent Unit (GRU) deep learningbased neural network models via the module of sequence-to-sequence regression are designed. In this sense, 180time series datasets of regional mean monthly meteorological, hydrological, and observed water table depths of 42 different monitoring piezometers during the period of Oct 2002-Sep 2017 are employed as the input variables. By using Shannon entropy method, the most influential parameters on GWL are determined as regional mean monthly air temperature (Tam), precipitation (Pm), total (sum) water diversion discharge (Wdm) of four main rivers. Nevertheless, Cosine amplitude sensitivity analysis confirmed Tam as a dominant factor. For preventing overfitting problem, an algorithm tuning technique via different kinds of hyperparameters is operated. In this respect, several scenarios are implemented and the optimal hyperparameters are accomplished via the trialand-error process. As stated by the performance evaluation metrics, Model Grading process, and Total Learnable Parameters (TLP) value, the innovative and unique suggested model (3), entitled GRU2+, (Double-GRU model coupled with Addition layer (+)) with seven layers is carefully chosen as the best model. The unique suggested model (3) in the optimal hyperparameters, resulted in an R2 of 0.91, a total grade (TG) of 7.76, an RMSE of 0.094 (m), and a running time of 47 (s). Thus, the model (3) can be certainly employed as an effective model to forecast GWL in different agricultural areas.