Ahmed Asal Kzar, Ahmed Asal Kzar and M Jafri, Mohd Zubir and Mutter, Kussay N. and Anwar, Saumi Syahreza
(2016)
A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping.
International Journal of Environmental Research and Public Health, 13 (92).
pp. 1-13.
ISSN 1661-7827
Abstract
Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems
can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield
Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the
total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia.
The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image
acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and
classify color satellite images. The samples were collected from the study area simultaneously with
the acquiring of satellite imagery. The sample locations were determined using a handheld global
positioning system (GPS). The TSS concentration measurements were conducted in a lab and used
for validation (real data), classification, and accuracy assessments. Mapping was achieved by using
the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2,
and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed
algorithm was investigated by dividing the validation data into two groups. The first group was
used as source samples for supervisor classification via the MHNNA. The second group was used to
test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes
were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated
between the two groups, according to their corresponding locations in the classes. The MHNNA
exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise,
where it proves its accuracy with noisy images over a range of noise levels. All results have been
compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water
in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote
sensing techniques (as based on ALOS images).
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