An approach based on Landsat images for shoreline monitoring to support integrated coastal management - a case study, Ezbet Elborg, Nile Delta, Egypt

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area

Conceptual prediction of harbor sedimentation quantities using AI approaches to support integrated coastal structures management

Sedimentation is one of the most critical environmental issues facing harbors’ authorities that results in significant maintenance and dredging costs. Thus, it is essential to plan and manage the harbors in harmony with both the environmental and economic aspects to support Integrated Coastal Structures Management (ICSM). Harbors' layout and the permeability of protection structures like breakwaters affect the sediment transport within harbors’ basins. Using a multi-step relational research framework, this study aims to design a novel prediction model for estimating the sedimentation quantities in harbors through a comparative approach based on artificial intelligence (AI) algorithms. First, one hundred simulations for different harbor layouts and various breakwater characteristics were numerically performed using a coastal modeling system (CMS) for generating the dataset to train and validate the proposed AI-based models. Second, three AI approaches namely: Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN) were developed to predict sedimentation quantities. Third, a comparison between the developed models was conducted using quality assessment criteria to evaluate their performance and choose the best one. Fourth, a sensitivity analysis was performed to provide insights into the factors affecting sedimentation. Lastly, a decision support tool was developed to predict harbors' sedimentation quantities. Results showed that the ANN model outperforms other models with mean absolute percentage error (MAPE) equals 4%. Furthermore, sensitivity analysis demonstrated that the main breakwater inclination angle, porosity, and harbor basin width affect significantly sediment transport. This research makes a significant contribution to the management of coastal structures by developing an AI data-driven framework that is beneficial for harbors' authorities. Ultimately, the developed decision-support AI tool could be used to predict harbors' sedimentation quantities in an easy, cheap, accurate, and practical manner compared to physical modeling which is time-consuming and costly. © 202

A novel AI approach for modeling land surface temperature of Freetown, Sierra Leone, based on land-cover changes

Land use/land cover (LULC) indices can be considered while developing land surface temperature (LST) models. The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes. This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon. Artificial neural network (ANN) and gene expression programming (GEP) were employed to develop LST prediction models. Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models. The extracted data of LULC indices, such as normal difference vegetation index (NDVI), normal difference built-up index (NDBI), urban index (UI), and normal difference water index (NDWI), were utilized as attributes to model LST. The results show that the root-mean-square error (RMSE) of the ANN and GEP models were 0.91oC and 1.08 oC, respectively. The GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation, which can be conveniently used to test new data regarding the thematic area. The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI, NDVI, and NDWI towards contributing LST