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Cumulative Infiltration And Infiltration Rate Prediction Using Optimized Deep Learning Algorithms: A Study In Western Iran, Mahdi Panahi, Khabat Khosravi, Sajjad Ahmad, Somayeh Panahi, Salim Heddam, Assefa M. Melesse, Ebrahim Omidvar, Chang Wook Lee
Cumulative Infiltration And Infiltration Rate Prediction Using Optimized Deep Learning Algorithms: A Study In Western Iran, Mahdi Panahi, Khabat Khosravi, Sajjad Ahmad, Somayeh Panahi, Salim Heddam, Assefa M. Melesse, Ebrahim Omidvar, Chang Wook Lee
Civil and Environmental Engineering and Construction Faculty Research
Study region: Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus: Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of …
Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang
Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang
Electrical & Computer Engineering Faculty Research
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of …
Deep Learning For Link Prediction In Dynamic Networks Using Weak Estimators, Carter Chiu, Justin Zhan
Deep Learning For Link Prediction In Dynamic Networks Using Weak Estimators, Carter Chiu, Justin Zhan
Computer Science Faculty Research
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization …