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Int. J. Environ. Res. Public Health 2016, 13(1), 92; doi:10.3390/ijerph13010092

A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping

1
School of Physics, Universiti Sains Malaysia, Penang 11800, Malaysia
2
Physics Department, Faculty of Science, Kufa University, Najaf 31001, Iraq
3
Physics Department, College of Education, Al-Mustansiriya University, Baghdad 10001, Iraq
*
Author to whom correspondence should be addressed.
Academic Editors: Yu-Pin Lin and Paul B. Tchounwou
Received: 10 August 2015 / Revised: 22 October 2015 / Accepted: 11 November 2015 / Published: 30 December 2015
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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). View Full-Text
Keywords: Hopfield neural network; remote sensing; ALOS; water quality mapping; TSS; environmental risk Hopfield neural network; remote sensing; ALOS; water quality mapping; TSS; environmental risk
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MDPI and ACS Style

Kzar, A.A.; Mat Jafri, M.Z.; Mutter, K.N.; Syahreza, S. A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping. Int. J. Environ. Res. Public Health 2016, 13, 92.

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