Flood forecasting for Melaka using arima and nar modelling methods

Flooding is an annual occurring incident in Malaysia. Several states in Malaysia are strongly affected by the flooding including Melaka where the flash floods are a common occurence. A flash flood is challenging to forecast and requires a sophisticated algorithm and system compared to the seasonal flood. It is difficult to forecast the flash flood compared to the seasonal flood. In Melaka, flash flood occurs regularly and it can happen to rise and fall in pace. This is the reason that flash floods can cause more damage than the seasonal floods. This study aims to develop a flood monitoring system to provide real-time data for the flood forecast. The objective is to develop a flood forecast model by analysing the flood parameters on a specific geographical layout which is Pengkalan Rama Jetty, Melaka. Following this, the efficiency of the flood forecast model is evaluated to forecast the water level where two flood forecast models were studied in this research which are the Autoregressive Integrated Moving Average (ARIMA) and Nonlinear Autoregressive Neural Network (NAR). The water level data considered for both methods were taken from 1st July 2020 at 12:00 am until 30th July 2020 at 7.00pm . There was a total of 2782 data in this time-series. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model. The second method using NAR as a flood forecast model. This research used the time series data in NAR training, validation and testing to forecast the flash flood. In this research, the model was set to forecast the water level in several hourly time period of 1, 3, 5, and 7 hours. The forecast accuracy were measured using the Pearson R and R-squared to find the most accurate model for this multiple time-step ahead. The model’s accuracy was determined by comparing the original and forecasted time series using Pearson R, R-Squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The result of the flood forecast system were compared with 7 hours forecast ahead and it was found that the ARIMA (2, 1, 3) was the best model for the Pengkalan Rama, Jetty, with an AIC of 5653.7004 and a BIC of 5695.209. The model also produced a lead forecast of up to 7 hours for the time series. Meanwhile, the result showed that the NAR model outperforms ARIMA with the lowest value in terms of RMSE, MSE, MAE and MAPE which are 1.915715, 3.669963, 1.576785 and 1.785951 respectively. In terms of Pearson R and R-Squared, the NAR model achieved Pearson R value of 0.931505 and R-Squared was 86.77024% compared to ARIMA which achieved R's value of -0.73993 and R-Squared of 54.74961%. It can be concluded that the flood forecast model for 7 hours ahead of using NAR outperformed ARIMA and is suitable for use in the flood forecast system at Pengkalan Rama Jetty

A SPACE FOR YOUTH, BY YOUTH? REASSEMBLING YOUNG SINGAPOREANS' PARTICIPATORY AND FRIENDSHIP PROCESSES THROUGH THE 2010 YOUTH OLYMPIC GAMES

Master'sMASTER OF SOCIAL SCIENCE

GA-Based Optimization for Multivariable Level Control System: A Case Study of Multi-Tank System

This paper presents a systematic way to determine the trade-off optimized controller tunings using computation optimization technique for both servo and regulatory controls of the Multi-Tank System, as one of the applications under the multivariable loop principle. The paper describes an improved way to obtain the best Proportional-Integral (PI) controller tunings in reducing the dependency on engineering knowledge, practical experiences and complex mathematical calculations. Relative Gain Array (RGA) calculation justified the degree of relation and the best pairing for both interacted control loops. Genetic Algorithm (GA), as one of the most prestigious techniques, was used to analyze the best controller tunings based on factor parameters of iterations, populations and mutation rates to the applied First Order plus Dead Time (FOPDT) models in the multivariable loop. Amid simulation analysis, GA analysis’s reliability was justified by comparing its performance with the Particle Swarm Optimization (PSO) analysis. The research outcome was visualized by generating the process responses from the LOOP-PRO’s multi-tank function, whereby the GA tunings’ responses were compared with the conventional tuning methods. In conclusion, the result exhibits that the GA optimization analysis has successfully demonstrated the most satisfactory performance for both servo and regulatory controls

Maps Preserving Schatten p

We study maps ϕ of positive operators of the Schatten p-classes (1<p<+∞), which preserve the p-norms of convex combinations, that is,   ∥tρ+(1-t)σ∥p=∥tϕ(ρ)+(1-t)ϕ(σ)∥p,  ∀ρ,σ∈p+(H)1,  t∈[0,1]. They are exactly those carrying the form ϕ(ρ)=UρU* for a unitary or antiunitary U. In the case p=2, we have the same conclusion whenever it just holds ∥ρ+σ∥2=∥ϕ(ρ)+ϕ(σ)∥2 for all the positive Hilbert-Schmidt class operators ρ,σ of norm 1. Some examples are demonstrated

Roundtable : Regional perspectives and politics = 圓桌討論 : 區域視野與政治

On 23rd July 2016, the first roundtable of SSFS3 was launched. Moderated by Ma Kwok Ming, this session consisted of 4 presentations by different speakers: Darwis Khudori, Pedro Páez, Wei Ran and Erebus Wong

Short-term Water Level Forecast Using ANN Hybrid Gaussian-Nonlinear Autoregressive Neural Network

The aim of this study is to develop the best forecast model using hybrid Gaussian-Nonlinear Autoregressive Neural Network to forecast the water level with multiple hour ahead for Melaka River.The&nbsp; development of flood forecast models is crucial and has led to risk control, policy recommendations, a reduction in human life loss, and a reduction in flood-related property destruction. In this research, Artificial Neural Network (ANN) approach was used to forecast flood by modeling and forecasting water level time series . ANN approach was selected due to its high reputation abilities to learn from the time-series data pattern. A total of&nbsp; 2782 data for the period of one month&nbsp; was used in ANN training, validation, and testing to forecast the flash flood. In this study , Hybrid Gaussian Nonlinear Autoregressive Neural Network (Gaussian-NAR) was used as the ANN approach to forecasting the water level time series. This study's primary focus is to find the most appropriate forecast model to forecast the water level in multiple time steps ahead, which are 1 hour, 3 hours, 5 hours, and 7 hours. The forecast accuracy measures are measured using the Pearson R and R-squared to find the most accurate model for this multiple time-step ahead. The result indicates that with 7 hours forecast ahead, the R squared is 86.7%. The best model in the Gaussian-NAR forecast is a 3-hour water level forecast with the R squared of 99.8 percent and had the best model performance result

Spatial analytical methods for deriving a historical map of physiological equivalent temperature of Hong Kong

Lai P-C, Choi CCY, Wong PPY, et al. Spatial analytical methods for deriving a historical map of physiological equivalent temperature of Hong Kong. Building and Environment. 2015;99:22-28