Geothermal energy is attracting more and more attention due to its large capacity and lack of dependency on the weather. Currently, many countries have planned enhanced geothermal system (EGS) projects. In this paper the first EGS project in Turkey, which is being implemented at the license area of SDS Energy Inc., in Dikili of the İzmir province, is introduced. Extensive geological, paleostress (279 fault-slip data from 33 locations), geophysical (magnetotelluric and vertical electrical sounding at 80 and 129 locations, respectively) and geochemical studies as well as paleostress measurements have been conducted in this area within the scope of this project. In the light of all these studies, it has been determined that the Dikili region is remarkable in terms of its high thermal gradient of about 7°C/100 m. The geothermal reservoir formation ''the Kozak granodiorite'' is a homogeneous, crystalline volcanic rock mass with high radiogenic heat production, and suitable for an EGS application. The analysis shows that the dominating fault system is normal, and the corresponding primary stress regime is extensional. Based on the geological, geophysical surveys and the estimated in situ stresses, numerical studies were carried out to assess the results of the hydraulic fracturing and geothermal energy production using the numerical codes FLAC3D plus and TOUGH2MP, respectively, in the area A of the Dikili site. The simulation results show that the stimulated reservoir volume and area could reach 44.5 million m 3 and 1 km 2 , respectively, with an injection volume of 122,931 m 3 . Assuming the fractured zone has a height of 1000 m and a half-length of 1200 m (the distance between injection and production wells being 1000 m), an average overall geothermal capacity of 83.7 MWth in 20 years could be reached with an injection rate of 250 l/s. The injection strategy and design parameters of the reservoir stimulation and geothermal production will be further optimized with the project running.

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bartın province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the 'seed cell' technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.

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