{"title":"Modeling of Annual Maximum Flows with Geographic Data Components and Artificial Neural Networks","authors":"Esra Aslı Çubukçu, Vahdettin Demir, M. F. Sevimli","doi":"10.26833/ijeg.1125412","DOIUrl":null,"url":null,"abstract":"Disasters such as floods and floods are also encountered on the days when the highest flow is recorded, according to the Annual Maximum Flow (AMF) statistics. The Annual Maximum Flow is the highest flow rate ever recorded in a water year. Wherever this flow happens, it usually results in flooding. Snow melts and unexpected precipitation associated with temperature fluctuations are the two most important factors that create flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of the flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. In this study, It is used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012.Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (8 Input) (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (2 Input) (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Engineering and Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26833/ijeg.1125412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Disasters such as floods and floods are also encountered on the days when the highest flow is recorded, according to the Annual Maximum Flow (AMF) statistics. The Annual Maximum Flow is the highest flow rate ever recorded in a water year. Wherever this flow happens, it usually results in flooding. Snow melts and unexpected precipitation associated with temperature fluctuations are the two most important factors that create flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of the flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. In this study, It is used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012.Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. Root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (8 Input) (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (2 Input) (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.
根据年度最大流量(AMF)统计,洪水和洪水等灾害也会发生在有记录的最高流量的日子里。年最大流量是在一个水年中有记录的最高流量。无论这种水流发生在哪里,通常都会导致洪水泛滥。融雪和与温度波动相关的意外降水是造成洪水的两个最重要因素。随之而来的洪水夺去了生命,摧毁了社区和农田的财产。因此,预测导致洪水的流量并采取适当的预防措施来限制损害是至关重要的。提前预测洪涝灾害的发生概率,对广大人民群众的生命财产安全和农用地安全具有重要意义。早期预警系统、灾害管理计划和尽量减少这些损失是该国政府的重要目标。在本研究中,它被用于位于土耳其Yeşilırmak盆地的五个当前观测站(COS)。利用这些站点的地理位置、海拔和面积信息等8个输入数据,尝试估算每个COS的AMF数据。本研究共使用了240个输入数据。数据期为1964-2012年。不幸的是,无法对2012年以后使用的所有5个台站的AMF值进行监测。因此,数据周期在2012年停止。本研究采用多层人工神经网络(MANN)、广义人工神经网络(GANN)、径向神经网络(RBANN)和多元线性调节(MLR)方法。输入数据集分成4个包,分别用于训练和测试阶段。以均方根误差(RMSE)、平均绝对误差(MAE)和相关系数(R)作为比较标准。研究结果如下:MANN(8个输入)(RMSE = 119.118, MAE = 93.213, R = 0.808)和RBANN(2个输入)(RMSE = 111.559, MAE = 81.114, R = 0.900)。这些结果表明,AMF可以用人工智能技术进行预测,并且可以作为一种替代方法。