This study presents a machine learning (ML)/artificial intelligence (AI)-based perspective to reliably predict and enhance the treatment efficiency of landfill leachate by classical-Fenton (c-Fenton) and photo-Fenton (p-Fenton) processes. This experiment also sought to lower treatment costs by evaluating the impact of using different numbers of UV-c (254 nm) lamps during p-Fenton processes, as well as to develop a sustainable process design for landfill leachate. In the modeling stage, the radial basis function neural network (RBFN), the feed forward neural network (FFNN), and the support vector regression (SVR) were used and the results were evaluated in a broad scanning. Our experimental results, optimized with the help of genetic algorithm (GA), showed an increasing trend in treatment efficiency and a decreasing trend in chemical usage amounts for p-Fenton oxidation. The results indicate that both treatment techniques performed (classical and p-Fenton) within 1 h contact time showed a very high pollutant removal with a reduction in COD of approximately 60% and 80%, respectively, during the first 30 min of processing. Additionally, it was noted that the COD elimination for the c-Fenton and the p-Fenton was significantly finished in first 15 min, 52% and 73%, respectively. According to the results of the optimization model, there is an increase from 62 to 82 percent under eight UV lamps compared to seven UV lamps when considering the impact of the number of UV lamps on the treatment efficiency in p-Fenton. It has been noted that when the results are taken as a whole, the better modeling abilities of ML-based models, particularly the RBFN and the FFNN, come to the fore. From a different angle, the FFNN and the RBFNN have both shown percentile errors that are extremely close to zero when MAPE values, a percentile error measure independent of the unit of the data set, are evaluated alone. Except for two tests whose desirability levels are still around 99.99%, all experiments attained outstanding desirability levels of 100.00%. This serves as more evidence for the higher modeling performance of these ML-based approaches.

The Ömerli Dam, supplied by multiple rivers that provide a major amount of Istanbul's water demands for drinking and irrigation, is experiencing toxicological threats due to rapid expansion, increased industrial activity, and population growth in light of the impending global drought. In this study, multivariate statistical analysis and spatial distribution based on geographic information system (GIS) was used to examine the status of pollution and health risk, irrigation water quality, and stochastic geo‐environmental risk sources of 16 potentially toxic elements (PTEs: Na, Mg, K, Ca, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Hg, and Pb) concentrations from samples in the dam. Also, spatial analyzing of quality indices was used to assess the dam's feasibility for irrigation and drinking. As per the results of the study, the dam water has mild heavy metal (loid) pollution, which is measured at 16 PTE by the heavy metal (loid) pollution index (HPI) and heavy metal (loid) evaluation index (HEI) values. The pollution level is low during the rainy season and moderate during the dry season. The water at sampling station S3 (HPI = 146.50) is unfit for consumption and cannot be used for drinking. The majority of harmful metal species exhibited elevated concentrations (notably Pb: 51.78 μg/L, Cr: 8.55 μg/L) in the dam lake water, particularly during the dry season. The toxic metals Pb and Cd account for 15.95% of the total variance, with a high degree of loading. Lead concentrations were found to be above WHO and USEPA limit values only in S2 and S4 during the wet season, but above 0.01 mg/L in all stations except S1 during the dry. Dam water is acceptable for agricultural use, according on the results of the, sodium absorption rate, the percentage of Na and the magnesium damage index, average SAR < 0.9, Na% < 25, and MH < 50, respectively. HQ _ingestion and HI values (all less than 1 in all PTEs) indicate that noncarcinogenic impacts might occur. Practitioner Points: Ömerli Dam provides most of the drinking and potable water needs of a mega city like Istanbul. With this study, toxic metal pollution of dam surface water and its possible effects on human health were analyzed for the first time on a spatially wide scale.

Bu çalışmada doğal ve yapay su kaynaklarının su kalitesini etkileyen etkenlerden birisi olarak saptanan deniz seviyesine olan yükseklikten kaynaklı meteorolojik değişimlerin CBS ortamında bir değerlendirmesi yapılmıştır. Ordu İli sınırları içerisinde 68 m rakıma sahip Gaga Gölü Sulak Alanı’nın ve 1732 m rakıma sahip Çambaşı Göleti’nin fiziksel ve kimyasal su kalitesi değerlerinin karşılaştırılması ve iki çalışma noktası arasındaki rakım farkının bu su ekosistemlerinin su kaliteleri üzerindeki etkileri mekânsal olarak analiz edilmiştir. Göllerdeki yüzey suyu kalitesinde oluşabilecek etkilerin daha iyi anlaşılabilmesi için 3 mevsim sürdürülen ölçüm çalışmalarından elde edilen bulgular dağılım haritaları vasıtasıyla karşılaştırılmıştır. Tüm veri analizi sonuçlarıyla beraber her iki su ekosisteminin bulunduğu bölge arasındaki meteorolojik, ekolojik ve sosyolojik farklılıklar göz önüne alınarak çevre kalite standartlarında görülen etkilerin değerlendirmesi yapılmış ve bölgeye özgü yoğun mevsimsel geçişler yaşanması nedeniyle bazı fiziko-kimyasal parametrelerin olumsuz yönde etkilendiği görülmüştür. Çalışmanın son bölümünde arazi kullanım farkı, noktasal ve yayılı kirletici kaynaklara olan yakınlığın oluşturduğu trofik düzeydeki değişimlerin sonuçları tartışılmış ve Yerüstü Su Kalitesi Yönetmeliği çerçevesinde sınıflandırma kriterleri göz önüne alınarak bu su kaynaklarının daha verimli kullanımı, korunması ve sürdürülebilir yönetimi konusunda bazı öneriler geliştirilmiştir.