Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling

Abstract

The complexities of coupled environmental and human systems across the space and time of fragile systems challenge new data-driven methodologies. Combining geographic information systems (GIS) and artificial neural networks (ANN) allows us to design a model that forecasts the erosion changes in Costa da Caparica, Lisbon, Portugal, for 2021, with a high accuracy level. The GIS–ANN model proves to be a powerful tool, as it analyzes and provides the “where” and the “why” dynamics that have happened or will happen in the future. According to the literature, ANNs present noteworthy advantages compared to the other methods that are used for prediction and decision making in urban coastal areas. In order to conduct a sensitivity analysis on natural and social forces, as well as dynamic relations in the dune–beach system of the study area, two types of ANNs were tested on a GIS environment: radial basis function (RBF) and multilayer perceptron (MLP). The GIS–ANN model helps to understand the factors that impact coastal erosion changes, and the importance of having an intelligent environmental decision support system to address these risks. This quantitative knowledge of the erosion changes and the analytical map-based frame are essential for an integrated management of the area and the establishment of pro-sustainability policies.

Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches

“…However, as introduced, performing such task using manual efforts is costly and automatic methods appear as an attractive alternative. In fact, over the years, several techniques [3,[14][15][16][17][18][19][20][21][22][23] have been proposed to perform erosion identification using remote sensing datasets.…”

“…In fact, the original formulation was altered to incorporate variations in rainfall erosivity and land-cover allowing the estimation of both spatial and temporal land-cover changes. Peponi et al [23] combined geographic information systems and shallow ANNs to design a model that forecasts the erosion changes using satellite images.…”

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

“…The Multilayer Perceptron is an artificial neural network (ANN) used in function approximation and pattern recognition and is made up of three components ( Figure 5) [66]. Artificial neural networks represent a simple way to mimic the neural system of the human brain, in which, through various samples-in this case, the training samples-one can recognize data which were previously unseen, and make decisions and solve problems regarding the spatial relationship/association between input variables and the presence or absence of a certain phenomenon [34,67,68].…”

“…Artificial neural networks represent a simple way to mimic the neural system of the human brain, in which, through various samples-in this case, the training samples-one can recognize data which were previously unseen, and make decisions and solve problems regarding the spatial relationship/association between input variables and the presence or absence of a certain phenomenon [34,67,68]. An MLP is based on the backpropagation algorithm-a supervised learning technique [66,69]. The neurons, represented by the variables/factors used in the analysis, are known as "input layers" and are connected to the "hidden layers" through a neural connection which holds the weights of the hidden layers.…”

Coastal Tidal Effects on Industrial Thermal Plumes in Satellite Imagery

“…Coastal zones are sensitive ecosystems providing valuable habitats for varied flora and fauna, as well as economic benefits including tourism and leisure. Coastal areas are constantly changing because of interactions with weather and coastal waters, climate change effects, sea-level changes, land-use impacts and human-made infrastructures [1,2]. Tourism, maritime traffic, fishing, human-made coastal installations, etc.…”