The Comparison of Spatial Models in Peak Ground Acceleration (PGA) Study

This study was conducted to compare the performance of three different spatial analysis models: Inverse Distance Weighted (IDW), Ordinary Kriging,­­ and Regularized Spline interpolation technique to determine the best fit model representing Peak Ground Acceleration (PGA) in West Java Province, Indonesia. The three models are commonly used in spatial visualization, but have different calculation methods. The calculations were performed using available formulas while the spatial modeling was conducted using the algorithms in GIS software. Meanwhile, the accuracy of the spatial model and factual calculation was determined through the Root Mean Square Error (RMSE). The results showed differences for both spatial distribution and maximum and minimum values for each model. However, IDW was observed to be the model which approaches the factual value of the PGA calculation as indicated by its RMSE value of 0.772352 in comparison with the 7.169879 (Ordinary Kriging) and 1.140802 (Regularized Spline).

MAPPING OF SALINITY LEVEL USING SPLINE INTERPOLATION TECHNIQUES OVER THE WATER OF SUNGAI MERBOK, KEDAH

Interpolation map of salinity is a helpful scientific instrument for environmental monitoring and for social economic development especially for the community who lived in Sungai Merbok. This research was conducted to develop a spatial model of salinity using spline interpolation technique. 20 sampling stations were randomly set up to measure the level of salinity using YSI 650 Multiparameter Display System (MDS). Quantitative analysis of standard regression and error index were used to evaluate the developed model. The research found that the tension splines type performed better than regularized splines type. The local government and community, who live in Sungai Merbok, can use the developed map of salinity for guidelines and future development of Sungai Merbok, Kedah.

Fuzzy Attribute Expansion Method for Multiple Attribute Decision-Making with Partial Attribute Values and Weights Unknown and Its Applications

In the real world, there commonly exists types of multiple attribute decision-making (MADM) problems with partial attribute values and weights totally unknown. Symmetry among some attribute information that is already known and unknown, and symmetry between the pure attribute set and fuzzy attribute membership set can be a considerable way to solve this type of MADM problem. In this paper, a fuzzy attribute expansion method is proposed to solve this type of problem based on two key techniques: the spline interpolation technique and the attribute weight reconfiguration technique, which are respectively used for the determination of attribute values and the reconfiguration of attribute weights. The spline interpolation technique to expand attribute values can enhance the performance of some regression methods and clustering methods by the comparisons between the results of these methods dealing with practical cases with and without the application of the technique, which further illustrates the effectiveness of this technique. For MADM problems with partial attribute values and weights totally unknown, compared with traditional fuzzy comprehensive evaluation (FCE), FCE with the application of fuzzy attribute expansion method can obtain results more similar with the ones when all attribute values and weights are known, which is proved by the practical power quality evaluation example.

Path Loss Modeling for Next Generation Wireless Network Using Fuzzy Logic - Spline Interpolation Technique

Appropriate path loss mathematical models help in reducing the complexity of the co-channel interference of Global System for Mobile Communication and Wireless Networks. This work proposes, to adopt and modify Hata model using fuzzy logic and spline interpolation techniques. Mean absolute errors of 1.55 dB and 0.4 dB were obtained using the techniques respectively. It was found that, using fuzzy logic - spline interpolation model, path loss is minimized by 1.94 dB in the study area. Therefore, fuzzy logic - spline interpolation is recommended for path loss prediction for next generation wireless networks in medium cities in Nigeria especially Yola. It is suggested that, neuro – fuzzy spline interpolation may be used to further minimize path loss error for medium cities.