Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been exten...Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been extensive research to model changes on the earth’s landscape including water bodies using remote sensing images. Meanwhile, meteorological data have been used mainly to model climate changes. This research tries to leverage both resources to achieve enhanced monitoring of the Dead Sea shrinkage: first, an attempt to model the relation between several meteorological variables and Dead Sea shrinkage using machine learning;second, formulating Dead Sea shrinkage in terms of water level and surface area using data extraction from remote sensing images;finally, confronting the two models to derive a novel approach for predicting Dead Sea shrinkage based on spatiotemporal images and meteorological measures. The main machine learning algorithms for modeling the water shrinkage in this empirical research are Decision Table, Linear Regression, and Multi Layer Perceptron Neural Networks. The Mean Absolute Error measure of the best model is 1.743 and 0.015. It is challenging to model the relation between meteorological variables and the water level. However, the obtained results are promising to formulate a model of the water level decline rate, which in its turn will be an essential tool for estimating the consumption limits and inflow needs to save the Dead Sea.展开更多
文摘Meteorological metrics have been used for weather forecasting and climate prediction. Remote sensing images proved to be a valuable resource to represent the terrain of earth’s surface. Recently, there has been extensive research to model changes on the earth’s landscape including water bodies using remote sensing images. Meanwhile, meteorological data have been used mainly to model climate changes. This research tries to leverage both resources to achieve enhanced monitoring of the Dead Sea shrinkage: first, an attempt to model the relation between several meteorological variables and Dead Sea shrinkage using machine learning;second, formulating Dead Sea shrinkage in terms of water level and surface area using data extraction from remote sensing images;finally, confronting the two models to derive a novel approach for predicting Dead Sea shrinkage based on spatiotemporal images and meteorological measures. The main machine learning algorithms for modeling the water shrinkage in this empirical research are Decision Table, Linear Regression, and Multi Layer Perceptron Neural Networks. The Mean Absolute Error measure of the best model is 1.743 and 0.015. It is challenging to model the relation between meteorological variables and the water level. However, the obtained results are promising to formulate a model of the water level decline rate, which in its turn will be an essential tool for estimating the consumption limits and inflow needs to save the Dead Sea.
