A hybrid climate model (HCM) is a novel proposed model based on the combination of self-organizing map (SOM) and analog method (AM). Model iklim hibrid (HCM) adalah model cadangan novel berdasarkan peta swaurus (SOM) dan kaedah analog (AM). Tujuan utama kajian ialah untuk meningkatkan ketepatan dalam peramalan curahan hujan menggunakan

The proper production plan plays an important role in the cashew nuts market enterprise in order to reduce cost. This study aims to find the optimal production plan for cashew nuts using ant lion optimization (ALO), symbiotic organisms search (SOS), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). The novel objective function is introduced in this study. Three input data set, including production cost, holding cost and inventory quantity are investigated. The experiment cases consist of the frequency of production cycle time in January, February and March, respectively. As a results, four algorithms are available to estimate not only the proper production plan of cashew nuts but also an ability in reducing the inventory and the holding costs. In summary, the ALO algorithm provides better predictive skill than others for the cashew nuts production plan with the lowest RMSE value of 0.0913.

The purpose of this study is to classify the pattern of rainfall stations in Thailand over the period from the year 2000 to 2009 using Self-Organizing Map (SOM). The number of stations is collected based on the complete rainfall data during rainy season. The optimal cases of learning rate function and different two SOM array sizes are investigated. The Gaussian function is applied as the neighborhood function. The results show that linear function and the 2×2 SOM array size are most suitable for classifying the pattern of rainfall. The similar characteristic of daily rainfall in each station is clustered into four patterns. Thus, SOM can be classified the similar rainfall stations, efficiently.

No abstract