Zalina Mohd Daud

A comparative study of mixed exponential and Weibull distributions in a stochastic model replicating a tropical rainfall process

A stochastic rainfall model is presented for the generation of hourly rainfall data in an urban area in Malaysia. In view of the high temporal and spatial variability of rainfall within the tropical rain belt, the Spatial–Temporal Neyman–Scott Rectangular Pulse model was used. The model, which is governed by the Neyman–Scott process, employs a reasonable number of parameters to represent the physical attributes of rainfall. A common approach is to attach each attribute to a mathematical distribution. With respect to rain cell intensity, this study proposes the use of a mixed exponential distribution. The performance of the proposed model was compared to a model that employs the Weibull distribution. Hourly and daily rainfall data from four stations in the Damansara River basin in Malaysia were used as input to the models, and simulations of hourly series were performed for an independent site within the basin. The performance of the models was assessed based on how closely the statistical characteristics of the simulated series resembled the statistics of the observed series. The findings obtained based on graphical representation revealed that the statistical characteristics of the simulated series for both models compared reasonably well with the observed series. However, a further assessment using the AIC, BIC and RMSE showed that the proposed model yields better results. The results of this study indicate that for tropical climates, the proposed model, using a mixed exponential distribution, is the best choice for generation of synthetic data for ungauged sites or for sites with insufficient data within the limit of the fitted region.

Optimal design of rain gauge network in Johor by using geostatistics and particle swarm optimization

This study proposes particle swarm optimization (PSO) approach to determine the optimal number and locations for the optimal rain gauge network in Johor state. The existing network of 84 rain gauges in Johor is also restructured into new locations by using daily rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and particle swarm optimization as the algorithm of optimization during the restructured proses. The numerical result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the proposed method can serve as an analysis tool for a decision making to assist hydrologist in the selection of prime sites for the installation of rain gauge stations.

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of ge...

Multi-dimensional reduction using self-organizing map

Self-Organising Map (SOM) is found to be a useful tool for climatological synoptic, analysis in extreme and rainfall pattern, cloud classification and climate change analysis. In data preprocessing for use in statistical downscaling, Principal Component Analysis (PCA) or empirical orthogonal function (EOF) analysis is used to select the mode criterion for the predictor and predictand fields for building a model. However, EOF contributes less total variance for most cases of which 70% to 90% of total population variance is accounted in the analysis. Therefore, SOM is proposed to obtain a nonlinear mapping for the preprocessing process. This study examines the dimension reduction of NCEP variable using SOM during the periods of November-December-January-February (NDJF). The NCEP data used is the 20 grids point atmospheric data for variable Sea Level Pressure (SLP). The result showed that SOM had extracted the high dimensional data onto a low dimensional representation.