Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

Abstract

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Assessment and monitoring of the Dead Sea surface area and water level using remote sensing and GIS techniques

“…In terms of freshwater in ow, the Jordan River contributes approximately 60%, while groundwater accounts for nearly 25% (Tierney et al, 2022). Moreover, it's crucial to note that the rate of the Dead Sea's retreat has been consistently diminishing, reaching up to 1 meter per year (Hamadneh et al, 2022). Additionally, the water surface level of the Dead Sea has experienced a continuous decline over the last three decades, recently exceeding 1 meter (Salameh et al, 2019; Ezersky et al, 2020).…”