A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

8th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Kzar, Ahmed Asal^1,2 ; Jafri, M. Z. Mat^1 ; San, Lim Hwee^1 ; Al-Zuky, Ali A.^3 ; Mutter, Kussay N.^1,4 ; Al-Saleh, Anwar Hassan^5

School of Physics, Universiti Sains Malaysia, Penang

11800, Malaysia^1

Physics Department, Faculty of Science, Kufa University, Najaf, Iraq^2

Physics Department, College of Science, Al-Mustansiriya University, Baghdad, Iraq^3

Physics Department, College of Education, Al-Mustansiriya University, Baghdad, Iraq^4

Department of Computer Science, College of Science, Al-Mustansiriya University, Baghdad, Iraq^5

关键词: Correlation coefficient;    Earth observation systems;    Environmental problems;    Minimum distance classifiers;    Neural network algorithm;    Remote sensing techniques;    Root mean square errors;    Water quality problems;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/37/1/012075/pdf
DOI  :  10.1088/1755-1315/37/1/012075

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.