Modified Hopfield Neural Network Algorithm (MHNNA) for TSS mapping in Penang strait, Malaysia

Abstract

The use of traditional ship sampling method of for environmental monitoring is time consuming, requires a high survey cost, and exert great efforts. In this study we classify one of the water pollutants which is the Total Suspended Solids (TSS) of polluted water in Penang strait, Malaysia by applying Modified Hopfield Neural Network Algorithm (MHNNA) on THEOS (Thailand Earth Observation System) image. The samples were collected from study area simultaneously with the airborne image acquisition. The samples locations were determined by using a handheld global positioning system (GPS), and the measurement of TSS concentrations was conducted in the lab as validation data (sea-truth data). By using algorithm (MHNNA) the concentrations of TSS have been classified according their varied values to produce the map. The map was colour-coded for visual interpretation. The investigation of efficiency of the proposed algorithm was based on dividing the validation data into two groups, the first group refers to standard samples for supervisor classification by the used algorithm. And the second group for test, where after classification we detect the second group data positions in the produced classes, then finding correlation coefficient (R) and root-mean-square-error (RMSE) between the first group data and the second group data according to their correspondence in the classes. The observations were high (R=0.899) with low (RMSE=17.687). This study indicates that TSS mapping of polluted water can be carried out using remote sensing technique by the application of MHNNA on THEOS satellite data over Penang strait, Malaysia.