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    A NEW ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF THE RAINFALL-RUNOFF RELATIONSHIP FOR LA CHARTREUX SPRING, FRANCE
    (Parlar Scientific Publications (P S P), 2018) Inan, Cagri Alperen; Canoglu, Mustafa Can; Kurtulus, Bedri
    The prediction of a rainfall-runoff relationship includes complex processes in karstic aquifer systems. In this study, an artificial neural network (ANN) model is utilized in order to simulate the rainfall -runoff relationships of La Chartreux spring in the karstic region Cahors, Southern France. Since numerical models are thought to be insufficient, the present study will contribute to the improvement of rainfall-discharge prediction models by using ANNs in MATLAB software. The model has been conducted with a feed forward and back propagation algorithm. The model is improved by the Levenberg-Marquardt algorithm in order to generalize the complex and non-linear rainfall-runoff issues. The meteorological data was obtained from meteorological stations in the region including eight years of rainfall and discharge data between 1976 and 1983. Model performance has been evaluated with respect to statistical error measures (root mean square error (RMSE), and correlation coefficient square (R-2). This study confirmed that artificial neural networks are capable of predicting rainfall-runoff relationships depending on the data quality, neural network properties, and data variability.
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    Öğe
    Land subsidence assessment under excessive groundwater pumping using ESA Sentinel-1 satellite data: a case study of Konya Basin, Turkey
    (Springer, 2021) Yesilmaden, Hande Mahide; Inan, Cagri Alperen; Kurtulus, Bedri; Canoglu, Mustafa Can; Avsar, Ozgur; Razack, Moumtaz
    Land subsidence analysis using satellite imagery is a consequential subject. Earth scientists have begun utilizing satellite imagery as an alternative to in-situ measurements and conceptual models. Synthetic aperture radar (SAR) images, moreover, utilize the reformer approach more than traditional satellite imagery with the use of high-resolution radar images. As a natural hazard, land subsidence is mostly attributed to excessive groundwater extraction, which is also the main reason for choosing the Konya Plain in Turkey as the study area for the present work. Since the Konya region is an agricultural and industrial land, groundwater extraction has been a challenging circumstance for the last few years. Change in groundwater level is also correlated with land subsidence rates through hydrogeological conceptualization. In this study, SAR images of the Sentinel 1 satellite are utilized for land subsidence rate calculation with the European Space Agency's SNAP software. Differential SAR interferometry (DInSAR) technique was used, which makes possible to detect deformation on the ground surface of the same portion of the Earth's surface using SAR images. The different acquisitions with DInSAR method allow to create differential interferograms that provide information ground motion with accuracy in cm. Three periods were utilized as 2016-2017, 2017-2018 and 2018-2019 the mean land subsidence rates were calculated for each period as 2.2, 1.4 and 1.7 cm/year, respectively. In the sum of the 3-year period, the maximum subsidence value went up to 16 cm.