Spectral Mixture Analysis-based WQI with Convolutional Long Short-Term Memory Techniques

Abstract: Surface water, including river water, is an important natural resource for human life. However, river water quality in Indonesia often declines due to various factors, such as excessive water consumption, waste pollution, and natural disasters. This study aims to predict the Water Quality Index (WQI) of rivers using Spectral Mixture Analysis with deep learning architecture. The methods used in this study are Spectral Mixture Analysis (SMA) and Convolutional Long Short-Term Memory (ConvLSTM). SMAs are used to decompose the spectral signatures of water quality components and provide insight into the composition of water bodies. ConvLSTM, a deep learning architecture, is used to capture temporal dependencies and spatial patterns in water quality data. The results showed that the percentage of WQI prediction accuracy for 345-band model was better than 234-band model, reaching 34.78%. The visible color spectrum that represents the Meets (M) and Light (R) Pollution Index is Blue (0, 0, 255) and wavelengths ranging from 0.53 μm to 0.88 μm. The test results of the ConvLSTM hybrid model on 8 mandatory parameters of River WQI measurements at 30 watershed monitoring points of North Musi Rawas Regency from 2021 to 2023, the accuracy value reaches 96% or it is considered that the performance of this model is acceptable. This research proves that Spectral Mixture Analysis with hybrid model Convolutional Long Short-Term Memory techniques is effectively capable of predicting and monitoring the WQI of rivers and these results can be used to take appropriate steps in determining policies.

Ika Oktavianti, Yusuf Hartono and Sukemi. “Spectral Mixture Analysis-based WQI with Convolutional Long Short-Term Memory Techniques”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150699

@article{Oktavianti2024,
title = {Spectral Mixture Analysis-based WQI with Convolutional Long Short-Term Memory Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150699},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150699},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Ika Oktavianti and Yusuf Hartono and Sukemi}
}