Browsing by Author "Alaboz, Pelin"

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An Artificial Intelligence Approach To the Assessment and Prediction of Soil Quality Dynamics
(Taylor & Francis Inc, 2025) Dengiz, Orhan; Alaboz, Pelin; Saygin, Fikret; Sargin, Bulut; Karaca, Siyami
The adverse effects of climate change, including land misuse, improper agricultural practices, and global warming, have a detrimental impact on soil health, fertility, productivity and quality. The degradation of soil, a fundamental component of the ecological system, poses a significant threat to the viability of sustainable land use practices, thereby impeding the rational and effective utilization of resources. Consequently, in order to ensure the sustainability of agricultural practices, it is essential to consider the reliability of soil quality determination methods and their suitability for large-scale implementation. The objective of this study was to predict soil quality using only the basic properties of soil (sand, clay, silt, organic matter, pH, electrical conductivity, lime, nitrogen, phosphorus, potassium) with artificial neural networks (ANN), one of the artificial intelligence algorithms that have attracted attention in recent years. The soil quality index (SQI) values of the soils within the Lake Van basin, which is characterized by a continental climate, were found to range between 0.381 and 0.703. Furthermore, the correlation coefficients (R) obtained between the actual data and the predicted data during the training, validation, and testing phases of the soil quality prediction with ANN were found to be 0.83, 0.83, and 0.71, respectively. The spatial distribution pattern of the actual and predicted values obtained in the SQI maps created using the Kriging-Simple-Spherical model, one of the geostatistical methods, in the study area, was found to be similar. The study demonstrated that incorporating additional soil properties into the model is essential for achieving more precise results.
Kumlu Tın ve Killi Tın Toprakta Kokopit Uygulamasının Tarla Kapasitesi ve Devamlı Solma Noktası Üzerine Etkisi
(2020) Çakmakcı, Talıp; Alaboz, Pelin
Toprak nem sabiteleri, sulama suyu miktarlarının belirlenmesinde kullanılan önemliparametrelerin başında gelmektedir. Bu çalışmada; kokopit uygulamalarının, farklı tekstürleresahip topraklarda suyun tutulması üzerine etkisi araştırılmıştır. Bu amaçla, kumlu tın ve killitın tekstürlü topraklara 4 farklı dozda (%0, 1, 2, 3) kokopit uygulanarak, 3 farklı süreyleinkübasyona [1 ay (T1), 2 ay (T2), 3 ay (T3)] bırakılmış ve toprakların tarla kapasitesi iledevamlı solma noktası belirlenmiştir. İncelenen özelliklerde inkübasyon süresine bağlı olarakbelirgin bir artış gözlenmemiş ve istatistiksel olarak önemli bulunmamıştır. Her iki tekstürgrubu için toprakların tarla kapasitesinde en yüksek artış (yaklaşık %8 oranında) %3 kokopituygulamasıyla T3 inkübasyon süresiyle sağlanmıştır. Toprakların devamlı solma noktasındaen belirgin artış kumlu tın tekstür grubunda %3 uygulamasının T3 inkübasyon süresinde(%3.81) bulunmuştur. Ayrıca, her iki tekstür grubu için materyalin ekonomik olarakuygulanabilirliği göz önüne alındığında devamlı solma noktasında optimum artış %2 kokopituygulamasıyla elde edilmiştir.
Pythagorean Fuzzy Swara Weighting Technique for Soil Quality Modeling of Cultivated Land in Semi-Arid Terrestrial Ecosystems
(Elsevier Sci Ltd, 2024) Sargin, Bulut; Alaboz, Pelin; Karaca, Siyami; Dengiz, Orhan
Currently, the assessment of soil quality and creating digital soil maps are crucial for sustainable land management. In the present study, the main objective is to evaluate soil quality around Lake Van's agricultural areas using Pythagorean Fuzzy SWARA (PF-SWARA) weighting for soil indicator assessment. Additionally, the predictability of soil quality is demonstrated through spatial distribution maps using random forest (RF) and artificial neural network (ANN) algorithms. PF-SWARA weighting assigns higher weights to indicators of physical quality. Soil quality index (SQI) values for the study area range between 0.36 and 0.74, classified as "from very low to high." RF and ANN models provide Lin's concordance correlation coefficient (LCCC) values of 0.93 and 0.87, respectively, for soil quality prediction. The RF model exhibits the lowest error rate (root mean square error (RMSE): 0.03; mean absolute percentage error (MAPE): 4.51%). The RF algorithm identified pH, available phosphorus, organic matter, CaCO3 and electrical conductivity as the most effective soil properties for estimating SQI. Ordinary Kriging geostatistical interpolation is identified as the interpolation method with the lowest RMSE value based on observed and predicted values' spatial distribution maps using Gaussian semivariogram from the geostatistical model. The study concludes that machine learning algorithms can be utilized alongside PF-SWARA approaches for digital soil quality mapping.