Yazar "Inalpulat, Melis" seçeneğine göre listele

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    Evaluation of the relationship between bacterial population and associated gas generation in soaking float of sheep skin using a sensor array system
    (INCDTP: Division Leather Footwear Research Institute, 2014) Kizil, Ünal; Yapici, Ali Nail; Yapici, Binnur Meriçli; Bilgi, Sadi Turgut; Inalpulat, Melis
    In this study it was aimed to design a metal oxide gas sensor array to determine the bacterial load in soaking float of wet-salted domestic sheep skin for garment leather production. The results showed that an array of 4 metal oxide gas sensors employed with Artificial Neural Networks (ANNs) can predict the 2 bacterial population in soaking float of leather manufacturing. The relationship between predicted and observed bacterial populations yielded a R value of 0.95 in model testing. Design procedures, gas sensors and other materials and techniques were explained in this paper.
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    Monitoring and multi-scenario simulation of agricultural land changes using Landsat imageries and future land use simulation model on coastal of Alanya
    (Pagepress Publ, 2024) Inalpulat, Melis
    Anthropogenic activities have adverse impacts on productive lands around coastal zones due to rapid developments. Assessment of land use and land cover (LULC) changes provide a better understanding of the process for conservation of such vulnerable ecosystems. Alanya is one of the most popular tourism hotspots on the Mediterranean coast of Turkey, and even though the city faced severe LULC changes after the mid-80s due to tourism-related investments, limited number of studies has been conducted in the area The study aimed to determine short-term and long-term LULC changes and effects of residential development process on agricultural lands using six Landsat imageries acquired between 1984 and 2017, and presented the first attempt of future simulation in the area. Average annual conversions (AAC) (ha) were calculated to assess magnitudes of annual changes in six different periods. AACs were used to calculate area demands for LULC2030 and LULC2050, whereby annual conversions from different periods were multiplied by the number of years between 2017, 2030 and 2050 for each scenario. Finally, optimistic and pessimistic scenarios for agricultural lands are simulated using a future land use simulation model. Accordingly, agricultural lands decreased from 53.9% to 31.4% by 22.5% in 33 years and are predicted to change between 19.50% and 24.63% for 2030, 1.07% and 14.10% for 2050, based on pessimistic and optimistic scenarios, respectively.
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    Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov-FLUS Model
    (Mdpi, 2024) Inalpulat, Melis
    Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, T & uuml;rkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov-FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region.
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    Subscale mapping of animal waste-based biogas potential and its equivalent energies using GIS: Canakkale, Türkiye
    (2023) Inalpulat, Melis
    The study presents the first attempt of determination and mapping of recent biogas potentials (BP) at different scales from province to village level in Çanakkale using Geographic Information Systems (GIS). The BP of different scales was calculated based on animal waste amounts from bovine, ovine and poultry farming. The study area covers the ten districts of Çanakkale province with the exception of Imbros and Tenedos Islands. The inventory records of different animal types were obtained from of Republic of Turkey Ministry of Agriculture and Forestry Çanakkale Directorate of Provincial Agriculture and Forestry. GIS procedures are conducted in ArcGIS (10.3) software. Findings revealed that the annual biogas production potential of the whole province is almost 6.4×107 m3. Biga district seemed to include 39 % of overall BP whereas Eceabat district presented a slight percentage of the potential production with the value of approximately 1 %. Moreover, the highest and lowest subscale-level potentials have found in Yukarıdemirci (Biga) and Bahçedere (Ayvacık), with approximately 154×104 m3 and 137 m3 BP, respectively. The overall BP of the province have concluded to be promising, and present study believed to serve as a baseline for future studies related to determination of new biogas plant suitable lands.
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    YIELD ESTIMATE USING SPECTRAL INDICES IN EGGPLANT AND BELL PEPPER GROWN UNDER DEFICIT IRRIGATION
    (Parlar Scientific Publications (P S P), 2014) Demirel, Kürşad; Genç, Levent; Bahar, Erdem; Inalpulat, Melis; Smith, Scott; Kızıl, Ünal
    The objective of this research was to estimate eggplant (Solanurn melongena cv. Aydin Siyahi') and bell pepper (Capsicum annuum L. California Wonder) yield using spectral indices. The experiments were conducted in the growing season of 2011 in Canakkale, Turkey. The following eight spectral indices were used (1) Simple Ratio Index (SR), (2) Normalized Difference Vegetation Index (NDVI), (3) Green Normalized Difference Vegetation Index (GNDVI), (4) Water Band Index (WBI), (5) Photochemical Reflectance Index (PRI), (6) Wide Dynamic Range Vegetation Index (WDRVI), (7) Enhanced Vegetation Indices (EVI) and (8) Red Edge Normalized Difference Vegetation Index (RENDVI). In addition to these indices the six following spectral bands were also used; (1) R-645, (2) R-663, (3) R-970, (4) R-940, (5) R-970/940 and (6) R-940/970. Four irrigation levels, including 0% (SO, non-irrigated), 33% (S33), 66% (S66) and 100% (S100, control) of field capacity were applied throughout three growth stages (vegetative (V), flowering (F) and fruit growth (FG)). Regression models were obtained between spectral indices and yield of both plants. The correlation coefficients (r) were between 0.921 and 0.997 for eggplant and between 0.857 and 0.900 for bell pepper at different growth stages. The research results showed that spectral indices and bands have a good potential to estimate the yield of eggplant and pepper plants.