Abstract
To simulate and predict the appropriate indices for algal blooms and petroleum pollution, this study combined remote sensing data and models of Machine Learning ML for the Aqaba Gulf’s condition. For algal blooms indication; floating algal index (FAI) was selected as the best index with 0.937 and for petroleum indices; ratio index (RI) was selected with 0.984. The collected data within the number of samples were separated into two parts: 80% for calibration to train and adjust the back propagation in neural network BPNN and partial least squares regression PLSR, and 20% for the external validation. Therefore, and based on the RI data, FAI was predicted using MLP, the obtained results showed that the ML algorithms gave models with high quality performance with R2= 0.955 and RMSE = 10.90. The PLSR and multilayer perceptron (MLP) were used to predict petroleum pollution using the extracted values to bands. The results showed that both models were obtained excellent models for predicting petroleum pollution. In general, MLP outperforms PLSR; within R²=0.941. Accordingly, the ML model was able to estimate the algal blooms and petroleum contamination with good accuracy. In the validation process, the determination coefficient R2 was 0.84 with an average square error equal to 0.076. As demonstrated, MLP could be a powerful mathematical tool for environmental analysis and prediction. The integration of remote sensing indices and data-driven statistical modeling were highly recommended for further similar studies.
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•Remote sensing data and Machine learning models including Multilayer Perceptron Neural Network (MLP) and partial least squares regression PLSR on the condition of the Aqaba Gulf.•This included Sentinel-2A satellite data. Some algal blooms, chlorophyll and seaweeds indices were selected in a comparison of petroleum contamination indices.•From algal blooms, Floating Algal Blooms (FAI) was selected as the best index with 0.937 and from petroleum indices, Ratio Index (RI) was selected with 0.984.•MLP well predicts many of the parameters investigated in this study, which means that MLP has the power to reveal complex and hidden relationships between parameters, which can be a powerful mathematical tool for environmental analysis.