This paper aims to evaluate the performance of the Long Short Term Memory (LSTM) model for flood forecasting. Seven data sets provided by the Drainage and Irrigation Department (DID) for Sungai Bedup, Serian, Sarawak, Malaysia are used for evaluating the performance of LSTM algorithm. Distinctive network was trained and tested using daily data obtained from the DID with the year range from 2014 to 2017. The performance of the algorithm was evaluated based on (Training Error Rate, Testing Error Rate, Loss, Accuracy, Validate Loss and Validate Accuracy) and compared with the Backpropagation Network (BP). Among the seven data sets, Sungai Bedup showed small testing error rate which is (0.08), followed by Bukit Matuh (0.11), Sungai Teb (0.14), Sungai Merang (0.15), Sungai Meringgu (0.12), Semuja Nonok (0.14) and lastly Sungai Busit is (0.13). Moreover, the developed model performance is evaluated by comparing with BP model. Results from this research evidently proved LSTM models is reliable to forecasting flood with the lowest testing error rate which is (0.08) and highest validate accuracy (92.61% ) compared to BP with testing error rate (0.711) and validate accuracy (85.00%). Discussion is provided to prove the effectiveness of the model in forecasting flood problems.
The aim of this article is to analyse the Deep Spiking Neural Network (DSNN) performance in flood prediction. The DSNN model has been trained and evaluated with 30 years of data obtained from the Drainage and Irrigation (DID) department of Sarawak from 1989 to 2019. The model's effectiveness is measured and examined based on accuracy (ACC), RMSE, Sensitivity (SEN), specificity (SPE), Positive Predictive Value (PPV), NPV and the Average Site Performance (ASP). Furthermore, the proposed model's performance was compared with other classifiers that are commonly used in flood prediction to evaluate the viability and capability of the proposed flood prediction method. The results indicate that a DSNN model of greater ACC (98.10%), RMSE (0.065%), SEN (93.50%), SPE (79.0%), PPV (88.10%), and ASP (89.60 %) is predictable. The findings were fair and efficient and outperformed the other BP, MLP, SARIMA, and SVM classification models.
Programming is a highly sought-after technical skill in the job market, but there are limited avenues available for training competent and proficient programmers. This research focuses on evaluating an immersive virtual reality (VR) application that has been introduced in the field of Python learning, which uses the interaction technique and a user interface, allowing the novice to engage in VR learning. 30 participants were recruited for the evaluation purpose and they are divided into two groups--15 for Experiment I, and 15 for Experiment II. A questionnaire to evaluate the user interface was done in Experiment I, and a questionnaire to evaluate the novice's acceptance of the VR application was given to the participants in Experiment II. Furthermore, interviews were conducted to collect detailed feedback from all the participants. From the results, it can be noted that the implemented interaction designs in this VR application are adequate. However, more interaction techniques can be integrated to increase the degree of immersive experience of the user in the application. Besides, the interface of the application is considered adequate and reasonable. Nevertheless, there is room for improvement in the aspect of usability and provide a higher level user experience. The novices' acceptance level of the new proposed learning method is low; this might be due to the users' fear of change--a normal human behaviour in embracing new things in life. Therefore, a larger sample size is proposed to further investigate the novice's acceptance of the new learning method by using an improved version of the VR application.
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