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- W3180597555 endingPage "105585" @default.
- W3180597555 startingPage "105585" @default.
- W3180597555 abstract "Urmia Lake in Northern Iran is drying up, which is causing significant environmental problems in the region, including saline storms that devastate agricultural land. We developed a remote sensing-based monitoring application to detect and map the location of saline flow sources with a novel automated deep learning convolutional neural network (DL-CCN). In order to train the model, we derived a normalised difference dust index (NDDI) from MODIS satellite images and collected ground control points (GCPs). These GCPs were randomly split for training (70%) and accuracy assessment (30%). We identified the following seven predisposing factors for saline flow source modelling: normalised difference vegetation index (NDVI), humidity percentage, temperature, wind speed, geomorphology, soil and land use/cover. In order to train the DL-CNN, we used ReLu, the root mean square error function, and Stochastic Gradient Descent (SGD) for the activation, loss/cost function, and optimization, respectively. Finally, we used the frequency ratio (FR) method to identify the most effective variable for the prediction of saline storm occurrences. The results reveal a high confidence (91.86% overall accuracy and a Kappa of 90.26) for the detection of saline flow sources. According to the FR model, the NDVI (0.982), humidity percentage (0.963), and land use/cover (0.925) are the most relevant factors for detecting the occurrence of saline storms in the Urmia Lake basin. In addition, we carried out a spatial uncertainty analysis of the results based on the Dempster Shafer Theory. The results will help the local stakeholders and decision-makers to better understating the saline flow sources and their respective environmental impacts." @default.
- W3180597555 created "2021-07-19" @default.
- W3180597555 creator A5003656810 @default.
- W3180597555 creator A5049774137 @default.
- W3180597555 creator A5056842687 @default.
- W3180597555 creator A5078698089 @default.
- W3180597555 date "2021-12-01" @default.
- W3180597555 modified "2023-09-23" @default.
- W3180597555 title "A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran" @default.
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- W3180597555 doi "https://doi.org/10.1016/j.catena.2021.105585" @default.
- W3180597555 hasPublicationYear "2021" @default.