IZTECH Research Centers Collection / İYTE Araştırma Merkezleri Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11147/2636
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Article Citation - WoS: 6Citation - Scopus: 6A Novel Land Surface Temperature Reconstruction Method and Its Application for Downscaling Surface Soil Moisture With Machine Learning(Elsevier, 2024) Güngör, Şahin; Güngör, Şahin; Gündüz, Orhan; Gündüz, Orhan; 03.10. Department of Mechanical Engineering; 03.07. Department of Environmental Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyDownscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage. © 2024 Elsevier B.V.Article Citation - WoS: 58Citation - Scopus: 59Assessment of Different Nanofiltration and Reverse Osmosis Membranes for Simultaneous Removal of Arsenic and Boron From Spent Geothermal Water(Elsevier, 2021) Jarma, Yakubu A.; Baba, Alper; Karaoğlu, Aslı; Tekin, Özge; Baba, Alper; Ökten, H.Eser; Tomaszewska, Barbara; Kabay, Nalan; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyOne of the factors that determine agricultural crops’ yield is the quality of water used during irrigation. In this study, we assessed the usability of spent geothermal water for agricultural irrigation after membrane treatment. Preliminary membrane tests were conducted on a laboratory-scale set up followed by mini-pilot scale tests in a geothermal heating center. In part I, three commercially available membranes (XLE BWRO, NF90, and Osmonics CK- NF) were tested using a cross-flow flat-sheet membrane testing unit (Sepa CF II, GE-Osmonics) under constant applied pressure of 20 bar. In part II, different spiral wound membranes (TR-NE90-NF, TR-BE-BW, and BW30) other than the ones used in laboratory tests were employed for the mini-pilot scale studies in a continuous mode. Water recovery and applied pressure were maintained constant at 60% and 12 bar, respectively. Performances of the membranes were assessed in terms of the permeate flux, boron and arsenic removals. In laboratory tests, the permeate fluxes were measured as 94.3, 87.9, and 64.3 L m?2 h?1 for XLE BWRO, CK-NF and NF90 membranes, respectively. The arsenic removals were found as 99.0%, 87.5% and 83.6% while the boron removals were 56.8%, 54.2%, and 26.1% for XLE BWRO, NF90 and CK-NF membranes, respectively. In field tests, permeate fluxes were 49.9, 26.8 and 24.0 L m?2 h?1 for TR-NE90-NF, BW30-RO and TR-BE-BW membranes, respectively. Boron removals were calculated as 49.9%, 44.1% and 40.7% for TR-BE-BW, TR-NE90-NF and BW30-RO membranes, respectively. Removal efficiencies of arsenic in mini-pilot scale membrane tests were all over 90%. Quality of the permeate water produced was suitable for irrigation in terms of the electrical conductivity (EC) and the total dissolved solids (TDS) for all tested membranes with respect to guidelines set by the Turkish Ministry of Environment and Urbanisation (TMEU). However, XLE BWRO, CK-NF and NF90 membranes failed to meet the required limits for irrigation in terms of boron and arsenic concentrations in the product water. The permeate streams of TR-BE-BW, TR-NE90-NF and BW30-RO membranes complied with the irrigation water standards in terms of EC, TDS and arsenic concentration while boron concentration remained above the allowable limit. © 2020 Elsevier B.V.