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Mohsen Farzin
Department of Range and Watershed Management, Faculty of Agriculture and Natural Resources, Yasouj University, Yasouj 75918-74934, Iran

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Journal article
Published: 21 January 2021 in Remote Sensing
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Nutrient input through submarine groundwater discharge (SGD) often plays a significant role in primary productivity and nutrient cycling in the coastal areas. Understanding relationships between SGD and topo-hydrological and geo-environmental characteristics of upstream zones is essential for sustainable development in these areas. However, these important relationships have not yet been completely explored using data-mining approaches, especially in arid and semi-arid coastal lands. Here, Landsat 8 thermal sensor data were used to identify potential sites of SGD at a regional scale. Relationships between the remotely-sensed sea surface temperature (SST) patterns and geo-environmental variables of upland watersheds were analyzed using logistic regression model for the first time. The accuracy of the predictions was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metric. A highly accurate model, with the AUC-ROC of 96.6%, was generated. Moreover, the results indicated that the percentage of karstic lithological formation and topographic wetness index were key variables influencing SGD phenomenon and spatial distribution in the northern coastal areas of the Persian Gulf. The adopted methodology and applied metrics can be transferred to other coastal regions as a rapid assessment procedure for SGD site detection. Moreover, the results can help planners and decision-makers to develop efficient environmental management strategies and the design of comprehensive sustainable development policies.

ACS Style

Aliakbar Samani; Mohsen Farzin; Omid Rahmati; Sadat Feiznia; Gholam Kazemi; Giles Foody; Assefa Melesse. Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf. Remote Sensing 2021, 13, 358 .

AMA Style

Aliakbar Samani, Mohsen Farzin, Omid Rahmati, Sadat Feiznia, Gholam Kazemi, Giles Foody, Assefa Melesse. Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf. Remote Sensing. 2021; 13 (3):358.

Chicago/Turabian Style

Aliakbar Samani; Mohsen Farzin; Omid Rahmati; Sadat Feiznia; Gholam Kazemi; Giles Foody; Assefa Melesse. 2021. "Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf." Remote Sensing 13, no. 3: 358.

Journal article
Published: 17 March 2020 in Applied Sciences
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Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

ACS Style

Viet-Ha Nhu; Saeid Janizadeh; Mohammadtaghi Avand; Wei Chen; Mohsen Farzin; Ebrahim Omidvar; Ataollah Shirzadi; Himan Shahabi; John J. Clague; Abolfazl Jaafari; Fatemeh Mansoorypoor; Binh Thai Pham; Baharin Bin Ahmad; Saro Lee. GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models. Applied Sciences 2020, 10, 2039 .

AMA Style

Viet-Ha Nhu, Saeid Janizadeh, Mohammadtaghi Avand, Wei Chen, Mohsen Farzin, Ebrahim Omidvar, Ataollah Shirzadi, Himan Shahabi, John J. Clague, Abolfazl Jaafari, Fatemeh Mansoorypoor, Binh Thai Pham, Baharin Bin Ahmad, Saro Lee. GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models. Applied Sciences. 2020; 10 (6):2039.

Chicago/Turabian Style

Viet-Ha Nhu; Saeid Janizadeh; Mohammadtaghi Avand; Wei Chen; Mohsen Farzin; Ebrahim Omidvar; Ataollah Shirzadi; Himan Shahabi; John J. Clague; Abolfazl Jaafari; Fatemeh Mansoorypoor; Binh Thai Pham; Baharin Bin Ahmad; Saro Lee. 2020. "GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models." Applied Sciences 10, no. 6: 2039.