{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:11:52Z","timestamp":1781107912709,"version":"3.54.1"},"reference-count":29,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<p>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\u20132005 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.<\/p>","DOI":"10.4018\/ijsir.2020070102","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T11:50:58Z","timestamp":1589889058000},"page":"19-29","source":"Crossref","is-referenced-by-count":6,"title":["Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm"],"prefix":"10.4018","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2170-2074","authenticated-orcid":true,"given":"Nawaf N.","family":"Hamadneh","sequence":"first","affiliation":[{"name":"Saudi Electronic University, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSIR.2020070102-0","unstructured":"Al-Hanbali, A., Al-Bilbisi, H., & Kondoh, A. (2005). The environmental problem of the Dead Sea using remote sensing and GIS techniques. Paper presented at the 11th CEReS International Symposium on Remote Sensing, Chiba University, Chiba, Japan. Academic Press. Retrieved from http:\/\/www2.cr.chiba-u.jp\/symp2005\/documents\/Postersession\/p017_AAlHanbali_paper.pdf"},{"key":"IJSIR.2020070102-1","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-012-0630-6"},{"key":"IJSIR.2020070102-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2007.06.002"},{"key":"IJSIR.2020070102-3","first-page":"1","article-title":"Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures.","author":"P. 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