Neurocomputing has been adjusted effectively in time series forecasting activities, yet the vicinity of exceptions that frequently happens in time arrangement information might contaminate the system preparing information. This is because of its capacity to naturally realise any example without earlier suspicions and loss of sweeping statement. In principle, the most widely recognised calculation for preparing the system is the backpropagation (BP) calculation, which inclines toward minimisation of standard slightest squares (OLS) estimator, particularly the mean squared mistake (MSE). Regardless, this calculation is not by any stretch of the imagination strong when the exceptions are available, and it might prompt bogus expectation of future qualities. In this paper, we exhibit another calculation which controls the firefly algorithm of least median squares (FFA-LMedS) estimator for neural system nonlinear autoregressive moving average (ANN-NARMA) model enhancement to provide betterment for the peripheral issue in time arrangement information. Moreover, execution of the solidified model in correlation with another hearty ANN-NARMA models, utilising M-estimators, Iterative LMedS and Particle Swarm Optimisation on LMedS (PSO-LMedS) with root mean squared blunder (RMSE) qualities, is highlighted in this paper. In the interim, the actual monthly information of Malaysian Aggregate, Sand and Roof Materials value was taken from January 1980 to December 2012 (base year 1980=100) with various levels of anomaly issues. It was found that the robustified ANN-NARMA model utilising FFA-LMedS delivered the best results, with the RMSE values having almost no mistakes at all in all the preparation, testing and acceptance sets for every single distinctive variable. Findings of the studies are hoped to assist the regarded powers including the PFI development tasks to overcome cost overwhelms.
A good quality of rainfall data is highly necessary in hydrological and meteorological analyses. Lack
of quality in rainfall data will influence the process of analyses and subsequently, produce misleading
results. Thus, this study is aimed to propose modified missing rainfall data treatment methods that
produced more accurate estimation results. In this study, the old normal ratio method and the
modified normal ratio based on trimmed mean are combined with geographical coordinate method.
The performances of these modified methods were tested on various levels of the missing data of 36
years complete daily rainfall records from eighteen meteorology stations in Peninsular Malaysia. The
results indicated that both modified methods improved the estimation of missing rainfall values at the
target station based on the least error measurements. Modified normal ratio based on trimmed mean
with geographical coordinate method is found to be the most appropriate method for station Batu
Kurau and Sg. Bernam while modified old normal ratio with geographical coordinate is the most
accurate in estimating the missing data at station Genting Klang.