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Piezometric depth modeling of groundwater using monthly variables of precipitation and water consumption (case study: Sarab Plain aquifer)
Piezometric depth modeling of groundwater using monthly variables of precipitation and water consumption (case study: Sarab Plain aquifer)
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Piezometric depth modeling of groundwater using monthly variables of precipitation and water consumption (case study: Sarab Plain aquifer)

Khosravi, Sepideh ; Fard, Ahmad Fakheri ; Dinpashoh, Yagob

Discover water, 2024-12, Vol.4 (1), p.14-12 [Peer Reviewed Journal]

Cham: Springer International Publishing

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2
Synthetic Time Series Data in Groundwater Analytics: Challenges, Insights, and Applications
Synthetic Time Series Data in Groundwater Analytics: Challenges, Insights, and Applications
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Synthetic Time Series Data in Groundwater Analytics: Challenges, Insights, and Applications

Pulla, Sarva T ; Yasarer, Hakan ; Yarbrough, Lance D

Water (Basel), 2024-04, Vol.16 (7), p.949 [Peer Reviewed Journal]

Basel: MDPI AG

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3
Short-Term Wind Speed Forecasting Using Nonlinear Autoregressive Neural Network: A Case Study in Kocaeli-Türkiye
Short-Term Wind Speed Forecasting Using Nonlinear Autoregressive Neural Network: A Case Study in Kocaeli-Türkiye
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Short-Term Wind Speed Forecasting Using Nonlinear Autoregressive Neural Network: A Case Study in Kocaeli-Türkiye

Gidom, Maysa ; Kökçam, Abdullah H. ; Uyaroğlu, Yılmaz

Electric power components and systems, 2024-02, Vol.52 (3), p.381-399 [Peer Reviewed Journal]

Philadelphia: Taylor & Francis

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4
Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting
Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting
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Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Nikentari, N ; Wei, H.-L

Institute of Advanced Engineering and Science 2024-02

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5
Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting
Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting
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Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting

Nikentari, Nerfita ; Wei, Hua-Liang

International journal of electrical and computer engineering (Malacca, Malacca), 2024-02, Vol.14 (1), p.960

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6
Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations
Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations
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Explainable boosted combining global and local feature multivariate regression model for deformation prediction during braced deep excavations

Zhang, Wenchao ; Shi, Peixin ; Wang, Zhansheng ; Zhao, Huajing ; Zhou, Xiaoqi ; Jia, Pengjiao

Engineering computations, 2023-12, Vol.40 (9/10), p.2648-2666 [Peer Reviewed Journal]

Bradford: Emerald Publishing Limited

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7
Enhancing Accuracy of Groundwater Level Forecasting with Minimal Computational Complexity Using Temporal Convolutional Network
Enhancing Accuracy of Groundwater Level Forecasting with Minimal Computational Complexity Using Temporal Convolutional Network
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Enhancing Accuracy of Groundwater Level Forecasting with Minimal Computational Complexity Using Temporal Convolutional Network

Haider, Adnan ; Lee, Gwanghee ; Jafri, Turab H. ; Yoon, Pilsun ; Piao, Jize ; Jhang, Kyoungson

Water (Basel), 2023-12, Vol.15 (23), p.4041 [Peer Reviewed Journal]

Basel: MDPI AG

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8
A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China
A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China
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A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China

Zhang, Jialun ; Dong, Donglin ; Zhang, Longqiang

Water (Basel), 2023-12, Vol.15 (23), p.4129 [Peer Reviewed Journal]

Basel: MDPI AG

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9
Prediction of Tail Water Level under the Influence of Backwater Effect Based on Deep Learning Models: A Case Study in the Xiangjiaba Hydropower Station
Prediction of Tail Water Level under the Influence of Backwater Effect Based on Deep Learning Models: A Case Study in the Xiangjiaba Hydropower Station
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Prediction of Tail Water Level under the Influence of Backwater Effect Based on Deep Learning Models: A Case Study in the Xiangjiaba Hydropower Station

Zhang, Sen ; Xie, Shuai ; Wang, Yongqiang ; Xu, Yang ; Zhang, Zheng ; Jia, Benjun

Water (Basel), 2023-11, Vol.15 (21), p.3854 [Peer Reviewed Journal]

Basel: MDPI AG

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10
Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions
Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions
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Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions

Katipoğlu, Okan Mert

Acta geophysica, 2023-10, Vol.71 (5), p.2265-2285 [Peer Reviewed Journal]

Cham: Springer International Publishing

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