{"title":"MEASUREMENT AND PREDICTION OF KARSTIC SPRING FLOW RATES","authors":"N. Darivianakis, K. Katsifarakis, M. Vafeiadis","doi":"10.30955/gnj.001499","DOIUrl":null,"url":null,"abstract":"This paper deals with prediction of the response of karstic springs by means of artificial neural networks (ANNs). A feed-forward back propagation ANN with three layers has been developed, to predict flow rates of two karstic springs, located at Rouvas area, Crete, Greece, using rainfall data as input. While the number of neurons of the input and output layers was determined by choice of data and desired output respectively, the number of neurons of the hidden layer was decided by means of numerous tests. Data used in ANN training and testing include daily and monthly precipitation depths (from September, 2006 to December, 2010) and measured flow rates of the two springs (from April, 2007 to December, 2010). Results show that the trained artificial neural network performed well, although flow rate measurements were not very regular. Moreover, the possibility of estimating the flow rate of one spring, based on measurements of the other has been investigated. Again the ANN gave satisfactory results. All spring flow rate and rainfall measurements are presented as an appendix, to facilitate further scientific research in the area of ANN application to water resources management.","PeriodicalId":55087,"journal":{"name":"Global Nest Journal","volume":"17 1","pages":"257-270"},"PeriodicalIF":1.2000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Nest Journal","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.30955/gnj.001499","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 1
Abstract
This paper deals with prediction of the response of karstic springs by means of artificial neural networks (ANNs). A feed-forward back propagation ANN with three layers has been developed, to predict flow rates of two karstic springs, located at Rouvas area, Crete, Greece, using rainfall data as input. While the number of neurons of the input and output layers was determined by choice of data and desired output respectively, the number of neurons of the hidden layer was decided by means of numerous tests. Data used in ANN training and testing include daily and monthly precipitation depths (from September, 2006 to December, 2010) and measured flow rates of the two springs (from April, 2007 to December, 2010). Results show that the trained artificial neural network performed well, although flow rate measurements were not very regular. Moreover, the possibility of estimating the flow rate of one spring, based on measurements of the other has been investigated. Again the ANN gave satisfactory results. All spring flow rate and rainfall measurements are presented as an appendix, to facilitate further scientific research in the area of ANN application to water resources management.
期刊介绍:
Global Network of Environmental Science and Technology Journal (Global NEST Journal) is a scientific source of information for professionals in a wide range of environmental disciplines. The Journal is published both in print and online.
Global NEST Journal constitutes an international effort of scientists, technologists, engineers and other interested groups involved in all scientific and technological aspects of the environment, as well, as in application techniques aiming at the development of sustainable solutions. Its main target is to support and assist the dissemination of information regarding the most contemporary methods for improving quality of life through the development and application of technologies and policies friendly to the environment