فهرست مقالات microscopic simulation

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  1 - Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network
  Soo See Chai Kok Luong Goh
  10.22094/joie.2021.1941252.1899
  Rainfall is a natural climatic phenomenon and prediction of its value is crucial for weather forecasting. For time series data forecasting, the Long Short-Term Memory (LSTM) network is shown to be superior as compared to other machine learning algorithms. Therefore, in چکیده کامل

  Rainfall is a natural climatic phenomenon and prediction of its value is crucial for weather forecasting. For time series data forecasting, the Long Short-Term Memory (LSTM) network is shown to be superior as compared to other machine learning algorithms. Therefore, in this research work, a LSTM network is developed to predict daily average rainfall values using meteorological data obtained from the Malaysian Meteorological Department for Kuching, Sarawak, Malaysia. Six daily meteorology data, namely, minimum temperature (°C), maximum temperature (°C), mean temperature (°C), mean wind speed (m/s), mean sea level pressure (hPa) and mean relative humidity (%) from the year 2009 to 2013 were used as the input of the LSTM prediction model. The accuracy of the predicted daily average rainfall was assessed using coefficient determinant (R2) and Root Mean Square Error (RMSE). Contrary to the common practice of dividing the whole available data set into training, validation and testing sub-sets, the developed LSTM model in this study was applied to forecast the daily average rainfall for the month December 2013 while training was done using the data prior of this month. An analysis on the testing data showed that, the data is more spread out in the testing set as compared to the training data. As LSTM requires the right setting of hyper-parameters, an analysis on the effects of the number of maximum epochs and the mini-batch size on the rainfall prediction accuracy were carried out in this study. From the experiments, a five layers LSTM model with number of maximum epoch of 10 and mini-batch size of 100 managed to achieve the best prediction at an average RMSE of 20.67 mm and R2 = 0.82. پرونده مقاله
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  2 - Modelling of Crowd Evacuation with Communication Strategy using Social Force Model
  Cheah Shiang Norhaida Hussain
  10.22094/joie.2021.1941247.1898
  Mobile crowd steering application has received much attention nowadays to steer crowds during fire or disaster evacuation. As reported in many studies, real drill experiments have been conducted to validate the mobile crowd steering application. However, simulations hav چکیده کامل

  Mobile crowd steering application has received much attention nowadays to steer crowds during fire or disaster evacuation. As reported in many studies, real drill experiments have been conducted to validate the mobile crowd steering application. However, simulations have also been undertaken to overcome the limitation of practical drill experiments when testing the application. Although there are algorithms reported for agent-based mobile crowd simulations, not many studies have adopted the social notion during mobile crowd steering simulations. As mobile crowd steering applications require user interaction during fire evacuation, we have foreseen a gap in current simulation algorithms, which leads to unrealistic simulation. This paper introduced a new insight into the agent-based crowd simulation through integration of communication strategy into the state of the art of social force for crowd management. The model was presented, formulated, and validated through a fire evacuation simulation. From the simulation results, the proposed model can reduce evacuation time and crowd density at the door opening area as compared to the original Social Force Model in a similar experimental setup پرونده مقاله