Melen nehri su kalitesinin istatistiksel analiz yöntemleri ve yapay zeka teknikleri kullanılarak değerlendirilmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada, Büyük Melen Nehri ve kollarında DSİ tarafından 5 örnekleme noktasından elde edilen 1995-2006 yılları arasındaki 26 fiziksel, kimyasal ve biyolojik parametreye ait ölçümler kullanılarak istatistiksel analiz yöntemleri ve yapay zeka teknikleri uygulamaları yapılmıştır. Ölçüm istasyonlarından elde edilen her bir veri setinde belirtici istatistiklerden ortalama, ortanca değer, tepe değeri ve dağılım ölçüleri grubuna giren varyans , standart sapma, standart hata gibi ölçüler belirlenmiştir. Su kalite verileri yüksek debili ve düşük debili dönem şeklinde 2 döneme ayrılmış, bu ayrım 11 yıl içindeki yüksek debili ve düşük debili dönemler, yağış ve debi verileri birlikte incelenerek belirlenmiştir. İstatistiksel analiz yöntemlerinden FA/TBA-ÇLR ve yapay zeka tekniklerinden KÖÖH-YSA kullanılarak yapılan analizlerde tüm istasyonların yüksek debili, düşük debili ve tüm dönemleri için ilişkili parametreler belirlenerek kirletici kaynakları belirleyen faktör/gruplar elde edilmiştir. Her iki yöntem için tüm istasyonlarda nehir sistemine etki edebilecek kirletici kaynaklardan nehrin mineral yapısı, bölgedeki toprak yapısı ve erozyonu, tarımsal faaliyetler, evsel ve kentsel deşarjlar ve foseptikler, kentsel yüzeysel akış, çiftlik hayvanları, katı atık depo alanları ve mevsimsel etki gibi kirletici kaynaklar belirlenmiştir. Ayrıca her bir faktör/gruba ÇLR ve YSA uygulanarak faktör/gruplar içindeki diğer parametreleri temsil eden etken parametreler belirlenmiştir. MTBS/ÇLR uygulaması ile her bir parametrenin konsantrasyonuna her bir kirletici kaynak bileşeninin lineer maddesel katkısı belirlenerek kaynak paylaşımı yapılmıştır. Bu uygulama sonuçları bulanık mantık uygulaması ile daha yalın ve anlaşılabilir bir biçime getirilerek hangi kirletici kaynağın hangi parametreyi hangi oranlarda etkilediği belirlenmiştir. Bu çalışmada, istatistiksel analiz ve yapay zeka tekniklerinin çok boyutlu veri setlerinin daha yorumlanabilir hale getirilmesi için kullanılabilirliği araştırılmıştır. Aynı zamanda su kalitesinin değerlendirilmesi ve yorumlanması, etkili kirletici parametrelerin ve kirletici kaynakların belirlenmesi, su kalitesinde etkili bir yönetim için çok değişkenli istatsitiksel yöntemler ve yapay zeka tekniklerinin etkili yöntemler olduğu sonucuna varılmıştır. Bu çalışmanın, havza izleme çalışmalarında özellikle anlık ve sürekli verilerin değerlendirilmesi ve yorumlanması açısından havza yöneticilerine, denetleyici ve akademik kurumlara fayda sağlayacağı düşünülmektedir.In this study, statistical analysis and artificial intelligence methods were employed to 26 physical and chemical pollution data obtained five monitoring stations on Big Melen River and its tributaries during the period 1995?2006 by State Hydraulic Works. Descriptive statistics such as mean, median, mode, variance, standard deviation, standard error were determined each data set. Water quality data were divided two part as high?low flow period and the periods were determined to investigate the high?low flow periods, rainy seasons and flow during 11 years. The PCA/FA and SOM-ANN was employed to evaluate the high?low flow periods correlations of water quality parameters, while the PCA and SOM techniques was used to extract the parameters that are most important in assessing high?low flow periods variations of river water quality. Factors/groups explained the pollution sources were identified as responsible for data structure at each data sets. So factors/groups are conditionally named mineral structure, soil structure and erosion, domestic, municipal and industrial effluents, agricultural activities (fertilizer, irrigation water), livestock wastes, waste disposal site and seasonal effects factors. PCA/FA and AOM were supported with MLR and ANN respectively, to determine the most important parameter in each factors/groups. APCS-MLR model were used for source apportionment and estimation of contributions from identified sources to the concentration of each parameter. APCS-MLR results was evaluation with fuzzy logic application to obtain comprehensible results for source apportionment and it was determined that which pollution sources affect the which parameters on which rate. The aim of this study is illustration the usefulness of multivariate statistical analysis and artificial intelligence for evaluation of complex data sets, in Melen River water quality assessment identification of factors/groups and pollution sources, for effective water quality management. It is thought that this study would suck advantage out of basin administrator, inspector and academic corporation with regard to evaluated and iterpreted especially continous and momentory data in basin monitoring studies

Assessment of lower Sakarya river water quality in terms of ırrigation water

Aşağı Sakarya Alt Havzası, Sakarya Nehrinin doğduğu noktadan itibaren maruz kaldığı sanayi faaliyetlerinin ve yerleşim yerlerinden kaynaklanan atıksuların, tarımsal ve kentsel yayılı kaynakların ve geçtiği jeolojik formasyonların etkisi altındadır. Tüm bu kirletici yüklerin etkisine rağmen geçtiği bölgelerde aynı zamanda tarımsal sulama maksadıyla da kullanılmaktadır. Bu nedenle, bu su kaynağının içme ve endüstriyel kullanımı yanında özellikle tarımsal amaçlı sürdürülebilir kullanımının önemi artmaktadır. Bu amaçla, çalışma kapsamında Aşağı Sakarya Nehrinin tarımsal amaçlı sulama suyu bakımından kalitesi belirlenmiştir. Aşağı Sakarya Nehri üzerinde Devlet Su İşleri (DSİ) tarafından işletilen 3 adet istasyona ait su kalite verilerine ait farklı fiziko-kimyasal özellikler farklı sulama suyu standartları açısından değerlendirilmiştir. Bu standartlar temelinde sulama suyu kalitesi, Elektriksel iletkenlik (Eİ), ve Toplam Sertlik (TS) değerleri ile Schoeller Diyagramı, Sodyum Adsorbsiyon Oranı (SAR), Sodyum yüzdesi (%Na), Magnezyum Oranı (MR), Potansiyel Tuzluluk (PS) ve Kelley indeksi (KI) gibi değerlendirme kriterleri kullanılarak yorumlanmıştırThe Lower Sakarya Sub-Basin is under the influence of the industrial activities and the wastewater from settlements, agricultural and urban run-off and the geological formations it has undergone since the birth of the Sakarya River. In spite of the effect of all these pollutant loads, it is also used for agricultural irrigation purposes. Therefore, the importance of sustainable use of this water resource for drinking and industrial use as well as for agricultural purposes is increasing. For this purpose, Lower Sakarya River quality of irrigation water for agricultural purposes has been determined. Different physical-chemical properties of water quality data belonging to 3 stations operated by State Hydraulic Works (DSI) on Sakarya River were evaluated in terms of national and international water quality standards. On the basis of these standards, irrigation water quality was interpreted by using evaluation criteria such as Electrical conductivity (EC), and Total Hardness (TH) values, Schoeller Diagram, Sodium Adsorption Rate (SAR), Sodium percentage (% Na), Magnesium Ratio (MR), Potential Salinity (PS) and Kelley index (KI)

Polimer adsorbsiyonu ile tekstil endüstrisi atıksularında renk giderimi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.ÖZET Anahtar Kelimeler: Tekstil Endüstrisi, Adsorbsiyon, Polimer, Renk Giderimi Türkiye endüstrilerden kaynaklanan su kirliliği tekstil merkezi olan bölgelerde önemli bir problemdir. Türkiye'de tekstil boyama ve son işlemlerden kaynaklanan atıksuların toplam hacmi yıda yaklaşık 150 milyon ton'dur. Çeşitli bölgelerimizde faaliyet gösteren tekstil fabrikaları önemli ve kullanımları gerekli olan bazı su ortamlarının kirlenmesine neden olmaktadır. Tekstil atıksularının iyileştirilmesi için firmalar fiziksel, kimyasal, biyolojik ve daha değişik arıtma sistemleri kullanmaktadırlar. Tekstil endüstrisi atıksularında boyarmaddeler önemli kirletici kaynaklarıdır. Boya maddelerinin tamamen parçalanması ise sadece kimyasal ve biyolojik oksitleme ile başarılabilmektedir. Bu çalışmada, adsorbsiyon yönteminde poliakrilamid kullanılarak tekstil endüstrisi atıksuyunda renk giderimi incelenmiştir. Adsorbent miktarı ve atıksu pH'ı değiştirilerek renk giderimi için optimize edilmiştir. Adsorbent miktarı 0,4 gr ve pH değeri 3 olduğunda renk giderimi için maksimum adsorbsiyon kapasitesi elde edilmiştir. Aynı zamanda çökeltme süresi 2-4 saat olduğunda renk giderim veriminin arttığı saptanmıştır. Sonuçta bu optimum şartlar sağlandığında rengin % 68'i giderilmiştir. Hide edilen bu değerlerle Lagergren ve Weber-Morris eşitliklerine göre adsorbsiyon hız sabiti (k) ve gözenek difüzyon hız sabiti (k ) hesaplanmıştır. Polimer adsorbsiyonu ile renk giderimi için en uygun adsorbsiyon izotermi Langmiur eşitliği ile eldç edilmiştir. vuıCOLOR REMOVAL IN TEX FILE INDUSTRY WASTEWATER BY POLYMER RESIN ADSORPTION SUMMARY Keywords: Textile industry, adsorption, polymer, color removal Water pollution originated from industries is a big problem in Turkey especially area of textile industry. The total volume of wastewater originated from textile dyeing and finishing processes in Turkey is arround 150 million tons/year. Textile firms studied at various regions pollute the important and usable water sources. Firms have started using physical, chemical, biological and different treatment systems for rehabilitation of textile wastewaters Dyestuff is important pollution source in textile industry wastewater. Degradation of dyestuff can be accomplished only by chemical and/or biological oxidation. In this study, the removal of color from textile industry wastewater was investigated using poliakrilamid for adsorption method. By changing the amount of adsorbent, mixing speed and wastewater pH, color removal was optimized. The maximum adsorption capacity for color removal was obtained, when the amount of adsorbent was 0,4 g, mixing speed was 1 50 rpm and at pH 3. At the same time, color removal efficiency increased when the precipitating time was 2-4 hours. As a result of this study, % 68 of color was removed at this optimum conditions. Adsorption rate constant (k) and pore difusion rate constant (k ) was calculated with the results according to Lagergren and Weber-Morris equations. Suitable adsorption isotherm for color removal with polymer adsorption was obtained by langmiur equation. I

Color removal from textile industry wastewater by polymer resin adsorption

The effectiveness of color removal from textile wastewaters by adsorption on polyacrylamide was investigated varying pH value, amount of adsorbent and agitation rate, the parameters important for the removal activity of the polymer resin. The adsorption followed the Langmuir isotherm. Optimum color removal efficiency (68%) was achieved at pH 3 using 0.4 g polyacrylamide agitated at 150 rpm, respectively. Simultaneously, color removal efficiency increased when the settling time was between 4 to 6 hours. These results indicate that polyacrylamide is one of the most effective polymer resins to remove colors from textile wastewaters

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. in order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction. (c) 2008 Elsevier Ltd. All rights reserved

Water Quality Assessment Using Artificial Intelligence Techniques: SOM and ANN-A Case Study of Melen River Turkey

Artificial intelligence methods have been employed with regard to 26 sets of physical and chemical pollution data obtained from the Melen River by the Turkish State Hydraulic Works during the period of 1995-2006. Water-quality data are divided into two parts relating to the high- and low-flow periods for the 1 KMP, 2 BMP, and 3 BMA stations. The self organizing map-artificial neural networks (SOM-ANNs) is employed to evaluate the high-low flow period correlations in terms of water-quality parameters. This is done in order to extract the most important parameters in assessing high-low flow period variations in terms of river water quality. The map size chosen is 9 x 9 in order to ensure that the maximum number of groups would be obtained from the training data. The groups explaining the pollution sources are identified as being responsible for the data structure at each dataset. The SOM, supported by ANN, is applied to provide a nonlinear relationship between input variables and output variables in order to determine the most significant parameters in each group. The multilayer feed-forward NN is chosen for this study. The most crucial parameters are determined, and the groups are conditionally named as mineral structure; soil structure and erosion; domestic, municipal, and industrial effluents; agricultural activity waste-disposal sites; and seasonal effects factors. Based on the explanation of the parameters, we can have an opinion about other parameters which can lead to cost and time savings. The aim of this study is to illustrate the usefulness of artificial intelligence for the evaluation of complex data in river- and water-quality assessment identification, and pollution sources, for effective water-quality management

Dissolved oxygen estimation using artificial neural network for water quality control

Dissolved oxygen (DO) is one of the key parameters when analyzing river water quality. Correct estimation of DO being carried by a river is very important for water quality control. DO is affected by lots of variables such as decomposition, nitrification, reaeration, sedimentation, photosynthesis, water discharge and temperature for that reason it is hard to solve such a complex problem. The methods available in the literature for DO estimation are complicated, time consuming and necessitate numbersome parameter estimation procedures. Artificial Neural Networks (ANNs) are simply mathematical representations of the functioning of the human brain. This paper examines the potential of ANN in estimating the DO from limited data (NO2-N, NO3-N, BOD, water discharge and temperature). This study employed feed forward (FF) type ANN for computing monthly values of DO. The results of the study clearly demonstrate that the ANN results are very close to the observed values of DO

Water Quality Assessment Using Multivariate Statistical Methods-A Case Study: Melen River System (Turkey)

This study is focused on water quality of Melen River (Turkey) and evaluation of 26 physical and chemical pollution data obtained five monitoring stations during the period 1995-2006. It presents the application of multivariate statistical methods to the data set, namely, principal component and factor analysis (PCA/FA), multiple regression analysis (MRA) and discriminant analysis (DA). The PCA/FA was employed to evaluate the high-low flow periods correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing high-low flow periods variations of river water quality. Latent factors were identified as responsible for data structure explaining 72-97% of the total variance of the each data sets. PCA/FA was supported with multiple regression analysis to determine the most important parameter in each factor. It examines the relation between a single dependent variable and a set of independent variables to best represent the relation in the each factor. Obtained important parameters provided us to determine the major pollution sources in Melen River Basin. So factors are conditionally named soil structure and erosion, domestic, municipal and industrial effluents, agricultural activities (fertilizer, irrigation water and livestock wastes), atmospheric deposition and seasonal effects factors. DA applied the data set to obtain the parameters responsible for temporal and spatial variations. Assessment of high-low flow period changes in surface water quality is an important aspect for evaluating temporal and spatial variations of river pollution. The aim of this study is illustration the usefulness of multivariate statistical analysis for evaluation of complex data sets, in Melen River water quality assessment identification of factors and pollution sources, for effective water quality management determination the spatial and temporal variations in water quality

Dissolved oxygen estimation using artificial neural network for water quality control

Dissolved oxygen (DO) is one of the key parameters when analyzing river water quality. Correct estimation of DO being carried by a river is very important for water quality control. DO is affected by lots of variables such as decomposition, nitrification, reaeration, sedimentation, photosynthesis, water discharge and temperature for that reason it is hard to solve such a complex problem. The methods available in the literature for DO estimation are complicated, time consuming and necessitate numbersome parameter estimation procedures. Artificial Neural Networks (ANNs) are simply mathematical representations of the functioning of the human brain. This paper examines the potential of ANN in estimating the DO from limited data (NO2-N, NO3-N, BOD, water discharge and temperature). This study employed feed forward (FF) type ANN for computing monthly values of DO. The results of the study clearly demonstrate that the ANN results are very close to the observed values of DO

Generic foresight model in changing hygiene habits with the pandemic: use of wet wipes in next generations

The vast use of wet wipes has now become a habitude, particularly following the altered perception of cleanliness during the pandemic and the encouragement towards using WW (wet wipe) to ensure parent's and children's hygiene. This study primarily aims to create a projection of the WW waste that will emerge in Turkey as a result of the promoted consumption by children who are predicted to retain the WW usage practices of their parents. In line with this habit adopted by children, the number of daily WW usage which is currently around 210 million is expected to rise to over 250 million between the years 2040 and 2060, depending on how the children are guided by their parent's existing habits. In this study, related calculations were made with FT-IR spectroscopy, taking into account the functional bond structure and percentage distribution of polymers in WWs. In this way, it is detected that 360 T, 568 T, and 623 T polymer materials would be thrown into the environment per day in 2021, 2040 and 2060, respectively. The damage of chemicals in WW content, employed at various concentrations, to the ecosystem structure is predicted and measures to be taken are outlined