Search

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. [Display omitted] • 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. [ABSTRACT FROM AUTHOR]

Predictions of Significant wave height (Hs) of oceans is highly required in advance for coastal and ocean engineering applications. Therefore, this study aims to precisely predict the ocean wave height via developing a novel hybrid algorithm. Wavelet, Particle Swarm Optimization (PSO), and Extreme Learning Machine (ELM) methods were used and integrated to design the wavelet-PSO-ELM (WPSO-ELM) model for estimating the wave height belongs to coastal and deep-sea stations. A comparative analysis among the ELM, Kernel ELM (KELM), and PSO-ELM models were performed with and without wavelet integration. In addition, wave height prediction time leads up to 72 h were assessed. The meteorological data, including wave height for one year, have been utilized and evaluated to design and validate the proposed model; the data obtained from buoys situated off the south-east coast of the US. The results demonstrated that the WPSO-ELM outperforms other models to predict the wave height in both hourly and daily lead times; in addition, the wavelet increased the accuracy of the prediction models, with the goal that coefficient of determination (R2), willmott's index of agreement (d), root mean square error (RMSE), and mean absolute error (MAE) were obtained for the lead time 12 h equivalent 0.794, 0.784, 0.374 m, and 0.297 m, respectively for the WPSO-ELM, and 0.643, 0.736, 0.495 m and 0.363 m, respectively for the PSO-ELM. Comparing the obtained results revealed the better performance of the WPSO-ELM model in predicting wave height for coastal and deep-sea regions up to 36 h' lead times. • WPSO-ELM is developed to estimate an accurate wave height at hourly and daily lead times. • The WPSO-ELM are evaluated at coastal and deep-sea zones. • WPSO-ELM, ELM, KELM, and PSO-ELM are compared to show the improvements of the proposed model. • WPSO-ELM outperforms other models in predicting wave heights for different zones up to 36-h lead times. [ABSTRACT FROM AUTHOR]