Zobrazeno 1 - 10
of 483
pro vyhledávání: '"Kwok‐Wing Chau"'
Autor:
Yi-yang Wang, Wenchuan Wang, Kwok-wing Chau, Dong-mei Xu, Hong-fei Zang, Chang-jun Liu, Qiang Ma
Publikováno v:
Journal of Hydroinformatics, Vol 25, Iss 6, Pp 2561-2588 (2023)
This article proposes a multi-head attention flood forecasting model (MHAFFM) that combines a multi-head attention mechanism (MHAM) with multiple linear regression for flood forecasting. Compared to models based on Long Short-Term Memory (LSTM) neura
Externí odkaz:
https://doaj.org/article/8fc12b807b0a4da4955c65b2e062197b
Publikováno v:
Journal of Hydroinformatics, Vol 25, Iss 3, Pp 943-970 (2023)
In runoff prediction, the prediction accuracy is often affected by the non-linear and non-stationary characteristics of the runoff series. In this study, a coupled forecasting model is proposed that decomposes the original runoff series by an improve
Externí odkaz:
https://doaj.org/article/c306ab37252045d0be29a0389f1ff01a
Autor:
Tao Hai, Hongwei Li, Shahab S. Band, Sadra Shadkani, Saeed Samadianfard, Sajjad Hashemi, Kwok-Wing Chau, Amir Mousavi
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 2206-2220 (2022)
Accurate estimation of the longitudinal dispersion coefficient (LDC) is essential for modeling the pollution status in rivers. This research investigates the capabilities of machine-learning methods such as multi-layer perceptron (MLP), multi-layer p
Externí odkaz:
https://doaj.org/article/2c381ca8d63a42469104d3c6126a7254
Autor:
Mojtaba Ghasemi, Mohammad-Amin Akbari, Changhyun Jun, Sayed M. Bateni, Mohsen Zare, Amir Zahedi, Hao-Ting Pai, Shahab S. Band, Massoud Moslehpour, Kwok-Wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 1483-1525 (2022)
The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these a
Externí odkaz:
https://doaj.org/article/a76c02c41f1248c5b1b9a00514b99e4e
Publikováno v:
Alexandria Engineering Journal, Vol 61, Iss 12, Pp 10631-10657 (2022)
In this research, monthly solar radiation is predicted in semi-dry, dry, and wet climates. Adaptive neurofuzzy interface system (ANFIS), radial basis function neural network (RBFNN), and multi-layer perceptron (MLP) models are used for predicting sol
Externí odkaz:
https://doaj.org/article/c7bd7a35ac48494ea58d5a5a462bf3ac
Autor:
Chengcheng Chen, Qian Zhang, Mahsa H. Kashani, Changhyun Jun, Sayed M. Bateni, Shahab S. Band, Sonam Sandeep Dash, Kwok-Wing Chau
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 248-261 (2022)
Applying data mining techniques for rainfall modeling because of a lack of sufficient memory components may increase uncertainty in rainfall forecasting. To solve this issue, in this research, a deep-learning-based long short-term memory (LSTM) model
Externí odkaz:
https://doaj.org/article/aded4340510e4f2e95f67795cb42fda9
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 713-723 (2022)
The machine learning method of Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a data-driven technique to model the dew point temperature (DPT). The input patterns, of T min, T max, and T mean, are utilized for the training. The results
Externí odkaz:
https://doaj.org/article/0d9c8e6e8c6f4e1294629e78a106153e
Autor:
Xiaoluan Zhang, Xinni Liu, Xifeng Wang, Shahab S. Band, Seyed Amin Bagherzadeh, Somaye Taherifar, Ali Abdollahi, Mehrdad Bahrami, Arash Karimipour, Kwok-Wing Chau, Amir Mosavi
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 764-779 (2022)
Dynamic viscosity of novel generated Copper Oxide (CuO)/Liquid Paraffin nanofluids is obtained experimentally for various temperatures and concentrations. To optimize the empirical process and for cost-efficiency, Feed-Forward Neural Networks (FFNNs)
Externí odkaz:
https://doaj.org/article/b83bf045348d43ce8da20ddcf59479e3
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 826-840 (2022)
Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as w
Externí odkaz:
https://doaj.org/article/ec9a99f8053748d98bf7e72613a64bf2
Autor:
Haiping Lin, Amin Gharehbaghi, Qian Zhang, Shahab S. Band, Hao Ting Pai, Kwok-Wing Chau, Amir Mosavi
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 16, Iss 1, Pp 1655-1672 (2022)
In this research, the mean monthly groundwater level with a range of 3.78 m in Qoşaçay plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) structures and a hybrid of variational mode decomposition with gated rec
Externí odkaz:
https://doaj.org/article/28467376a4294374ae3d582b09b39629