Ali Ercan

E-mail

ercanali@metu.edu.tr

Department

Department of Civil Engineering

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling Ishida, Kei; Ercan, Ali; Nagasato, Takeyoshi; Kiyama, Masato; Amagasaki, Motoki (2024-05-01)
Extensions of Navier-Stokes-Euler Governing Equations of Fluid Flow to Fractional Time and Multi-Fractional Space Kavvas, M. Levent; Ercan, Ali (2024-01-01) This paper describes the recently developed governing equations of unsteady multi-dimensional incompressible and compressible flow in fractional time and multi-fractional space. When their fractional powers in time and in ...
Smart Thinking on Co-Creation and Engagement: Searchlight on Underground Built Heritage Smaniotto Costa, Carlos; Volzone, Rolando; Ruchinskaya, Tatiana; Solano Báez, Maria del Carmen; Menezes, Marluci; Ercan, Ali; Rollandi, Annalisa (2023-02-01) This paper aims to explore public participation for activating underground built heritage (UBH). It describes and analyses practices of stakeholders’ engagement in different UBH assets, based on experiences gathered in the...
Multidimensional Governing Equations of Matrix Flow Component of Subsurface Stormflow as Function of Bedrock Surface Geometry Kavvas, M. Levent; Tu, Tongbi; Ercan, Ali; Chen, Z. Q. (2022-12-01) Subsurface flow is a critical component in the hydrological cycle, since it controls the quantity and timing of surface runoff and groundwater flow. Field studies have shown the fundamental influence of the bedrock surface...
Generalizations of incompressible and compressible Navier–Stokes equations to fractional time and multi-fractional space Kavvas, M. Levent; Ercan, Ali (2022-12-01) This study develops the governing equations of unsteady multi-dimensional incompressible and compressible flow in fractional time and multi-fractional space. When their fractional powers in time and in multi-fractional spa...
Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM Yokoo, Kazuki; Ishida, Kei; Ercan, Ali; Tu, Tongbi; Nagasato, Takeyoshi; Kiyama, Masato; Amagasaki, Motoki (2022-01-01) ABSTR A C T This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long s...
Multi-time-scale input approaches for hourly-scale rainfall-runoff modeling based on recurrent neural networks Ishida, Kei; Kiyama, Masato; Ercan, Ali; Amagasaki, Motoki; Tu, Tongbi (2021-11-01) This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as in...
Comparison of three recurrent neural networks for rainfall-runoff modelling at a snow-dominated watershed Yokoo, K.; Ishida, K.; Nagasato, T.; Ercan, Ali; Tu, T. (2021-10-25) © Published under licence by IOP Publishing Ltd.In recent years, rainfall-runoff modelling using LSTM has shown high adaptability. However, LSTM requires far more computational costs than traditional RNN. In addition, a di...
Hybrid precipitation downscaling over coastal watersheds in Japan using WRF and CNN Tu, Tongbi; Ishida, Kei; Ercan, Ali; Kiyama, Masato; Amagasaki, Motoki; Zhao, Tongtiegang (2021-10-01) Study region: Kuma River Watershed in Japan.
Space and Time Fractional Governing Equations of Unsteady Overland Flow Kavvas, M. Levent; Ercan, Ali; Tu, Tongbi (2021-07-01) Combining fractional continuity and motion equations, the space and time fractional governing equations of unsteady overland flow were derived. The kinematic and diffusion wave approximations were obtained from the space a...

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