Modeling river suspended sediment yield using fuzzy logic

Nehirlerdeki askı malzemesi miktarının doğru tahmini; kirliliğin belirlenmesi, akarsu taşımacılığı, baraj ömrünün tespiti, hidroelektrik teçhizatlarının emniyeti, balıkların yaşamını sürdürmesi, nehrin estetiğinin korunması vb. konularda çok önemlidir. Bu amaçla kullanılan katı madde anahtar eğrileri, çoğu tahminlerde iyi sonuçlar vermemelerine rağmen yaygın bir kullanım alanına sahiptirler. Bu çalışmada nehir enkesitindeki askı maddesi miktarının belirlenmesi için katı madde anahtar eğrilerine göre çok daha iyi bir alternatif olarak bulanık mantığa dayalı modeller geliştirilmiştir.Geliştirilen bulanık modeller USGS (United States Geological Survey) tarafından işletilen iki istasyonun günlük gerçek zaman debi ve askı malzemesi konsantrasyonu verilerine uygulanmış ve katı madde anahtar eğrileri ile karşılaştırılmıştır.Sonuçta bulanık modellerin, askı malzemesini modellemede katı madde anahtar eğrilerine göre daha iyi sonuçlar verdiği görülmüştür.Anahtar Kelimeler: Askı malzemesi konsantrasyonu, bulanık mantık, diferansiyel evolüsyon, katı madde anahtar eğrisi, modelleme.Correct estimation of sediment volume carried by a river is important with respect to pollution, channel navigability, reservoir filling, hydroelectric-equipment longevity, fish habitat, river aesthetics and scientific interests. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this study, some models based on fuzzy logic is developed as a superior alternative to the sediment rating curve technique for determining suspended sediment concentration for a given river cross-section. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating curve models. Benchmarking was based on five-year period of continuous streamflow and sediment concentration data of Quebrada Blance Station and four-year period of data of Rio Valenciano Station operated by the United States Geological Survey (USGS). Nine different fuzzy models were established for each station to estimate sediment concentration from streamflow. Each fuzzy model had different number of membership functions. Parameters of membership functions are found using differential evolution algorithm. The benchmark results show fuzzy models produce much better results than rating curve models.Keywords: Suspended sediment concentration, fuzzy logic, differential evolution, sediment rating curve, modeling

Analiza trenda mjesečnih strujanja Şenovom inovativnom metodom trenda

Trend analysis of monthly mean streamflows is essential for better water resources management and planning. In this study, Mann Kendall (MK), Sen’s method and Şen’s innovative trend method (ITM) were employed in order to examine the possible trends of monthly streamflows obtained from nine stations from three basins (Yakabasi and Derecikviran in Western Black Sea Basin; Durucasu, Sütlüce, Kale and Gomeleonu in Yesilirmak Basin; Şimşirli, Tozköy and Topluca in Eastern Black Sea Basin) located in Black Sea Region of Turkey. Based on the MK, streamflow data of Tozköy Station which is located in western part of the Eastern Black Sea Region showed a significantly increasing trend while a significantly decreasing trend was found for the Yakabasi, Derecikviran, Durucasu and Sütlüce stations which are situated in western part of the Black Sea Region. According to the Sen’s trend method, a significantly decreasing trend was seen in Durucasu, Sütlüce, Yakabasi and Derecikviran stations while Tozköy station showed significantly increasing trend. According to the ITM, low-medium values of Tozköy Station indicated slightly increasing trend while low and medium streamflow values of Yakabasi, Derecikviran, Durucasu and Sütlüce stations showed a decreasing trend. High streamflow values of Derecikviran and Sütlüce stations showed a decreasing trend while corresponding values of Yakabasi, Şimşirli and Tozköy stations indicated an increasing trend. It was showed that trends of low, medium, and high data can be easily identified by ITM which has some advantages (having no assumption such as serial relationship, non-normality, and, test number) over the Sen’s method and Mann-Kendall test.Analiza trenda srednjih mjesečnih strujanja je neophodna za bolje upravljanje vodama i planiranje. U ovom su istraživanju korišteni: Mann-Kendallova test (MK), Senova metoda i Şenova inovativna metoda trendova (ITM) kako bi se ispitali mogući trendovi mjesečnih strujanja dobivenih s devet postaja u tri bazena (Yakabasi i Derecikviran u zapadnom slivu Crnog mora, Durucasu, Sütlüce, Kale i Gomeleonu u slivu Yesilirmak, Şimşirli, Tozköy i Topluca u istočnom slivu Crnog mora) koji se nalaze u crnomorskoj regiji Turske. Na temelju MK, podaci strujanja s postaje Tozköy, koja se nalazi na zapadnom dijelu istočne crnomorske regije, pokazali su znatno povećanje trenda, dok su za Yakabasi, Derecikviran, Durucasu i Sütlüce u zapadnom dijelu crnomorske regije uočeni znatno opadajući trendovi. Prema Senovoj metodi trenda, znatno je smanjen trend na postajama Durucasu, Sütlüce, Yakabasi i Derecikviran, dok je postaja Tozköy pokazala znatno povećanje trenda. Prema ITM-u, niske i srednje vrijednosti postaje Tozköy pokazuju neznatno povećanje trenda, dok su niske i srednje vrijednosti strujanja za Yakabasi, Derecikviran, Durucasu i Sutluce pokazale trend smanjenja. Visoke vrijednosti strujanja Derecikviran i Sütlüce postaja pokazale su trend smanjenja, dok su odgovarajuće vrijednosti za Yakabasi, Şimşirli i Tozköy pokazale rastući trend. Pokazalo se da ITM može lako identificirati trendove niskih, srednjih i visokih vrijednosti podataka, što ima neke prednosti u odnosu na Senovu metodu i Mann-Kendallov test (nema pretpostavki poput serijskog odnosa, nenormalnosti i testnog broja)

Usporedna analiza modela za prognozu koncentracija ozona pomoću evolucijskog programiranja gena i višestruke linearne regresije

ground-level ozone (O3) has been a serious air pollution problem for several decades and in many metropolitan areas, due to its adverse impact on the human respiratory system. Therefore, to reduce the risks of O3 related damages, developing, maintaining and improving short term ozone forecasting models is needed. This paper presents the results of two prognostic models including gene expression programming (gEP), which is a variant of genetic programming (gP), and multiple linear regression (MLR) to forecast ozone levels in real-time up to 6 hours ahead at four stations in Bilbao, Spain. The inputs to the gEP were meteorological conditions (wind speed and direction, temperature, relative humidity, pressure, solar radiation and thermal gradient), hourly ozone levels and traffic parameters (number of vehicles, occupation percentage and velocity), which were measured in the years of 1993–94. The performances of developed models were compared with observed values and were evaluated using specific performance measurements for the air quality models established in the Model Validation Kit and recommended by the US Environmental Protection Agency. It was found that the gEP in most cases gives superior predictions. Finally it can be concluded on the basis of the results of this study that gene expression programming appears to be a promising technique for the prediction of pollutant concentrations.Zbog štetnog utjecaja na dišni sustav prizemni ozon (O3) već nekoliko desetljeća predstavlja ozbiljan problem u mnogim onečišćenim urbanim područjima. Kako bi se smanjili rizici od oštećenja uzrokovanih ozonom, potrebno je razvijati, održavati i poboljšavati modele kratkoročne prognoze ozona. Ovaj rad prikazuje rezultate dvaju prognostičkih modela, evolucijskog programiranja gena (GEP), koje je varijanta genetskog programiranja (GP), te prognoziranje razina ozona u realnom vremenu višestrukom linearnom regresijom (MLR) do šest sati unaprijed na četiri postaje u Bilbau u Španjolskoj. Ulazni podaci za GEP su meteorološki uvjeti (brzina i smjer vjetra, temperatura, relativna vlažnost zraka, tlak, sunčevo zračenje i termički gradijent), satne razine ozona i parametri prometa (broj vozila, udio vremena zauzetosti ceste vozilima i njihova brzina), koji su izmjereni u razdoblju 1993–1994. Performanse razvijenih modela ocijenjene su usporedbom s mjerenjima te upotrebom alata za validaciju modela koje je predložila američka Agencija za zaštitu okoliša. Utvrđeno je da GEP u većini slučajeva daje bolje prognoze. Na kraju je zaključeno da je evolucijsko programiranje gena obećavajuća tehnika za prognozu koncentracija onečišćujućih tvari

Estimate of Daily Evaporation through Sugeno Fuzzy System

The process by which water changes from a liquid into a gas or vapor is called evaporation. As the climatic conditions keeps steadily changing, it is expected that evaporation from terrestrial water bodies (oceans, seas, lakes, and rivers) and soils will increase. Such a change can cause environmental and socioeconomic impacts in a global or local scale. Therefore, it is imperative that the water loss due to evaporation be estimated on a rational basis, especially for monitoring, surveying, and managing water resources in arid lands. A study is performed herein for this purpose. Sugeno fuzzy system is considered by using the adaptive network based fuzzy inference system (ANFIS) toolbox in MATLAB to estimate the daily evaporation of water. Such climatic factors as air temperature, solar radiation, wind speed, atmospheric pressure, and humidity are employed as input into the models. Various combinations of these daily variables are constituted in order to evaluate the extent of effect of each of these parameters on daily evaporation. The estimates of Sugeno-type fuzzy inference systems are compared. The Stephens-Steward (SS) method and multi-linear regression are also considered for additional comparisons. The values of evaporation computed from these three procedures are evaluated by using various statistical means. Based on the comparisons made, it is concluded that the ANFIS could be successfully utilized for modeling the daily process of evaporation.</p

Comparison of three back-propagation training algorithms for two case studies

434-442This paper investigates the use of three back-propagation training algorithms, Levenberg-Marquardt, conjugate gradient and resilient back-propagation, for the two case studies, stream-flow forecasting and determination of lateral stress in cohesionless soils. Several neural network (NN) algorithms have been reported in the literature. They include various representations and architectures and therefore are suitable for different applications. In the present study, three NN algorithms are compared according to their convergence velocities in training and performances in testing. Based on the study and test results, although the Levenberg-Marquardt algorithm has been found being faster and having better performance than the other algorithms in training, the resilient back-propagation algorithm has the best accuracy in testing period

Ten-stage discrete flood routing for dams having gated spillways

Ten-stage operation policies for routing of flood hydrographs from very small magnitudes up to the probable maximum flood (PMF) for any dam having a gated spillway are suggested. The gate opening rules are determined based on the recent pool level. Without having to predict the magnitude of the incoming flood hydrograph beforehand, the fixed rules of the ten-stage operation policy will provide quasi-optimum routing for any floods, which are classified into 10 different groups. 0.1 PMF, 0.2 PMF,..., and PMF are the upper limits of the ten groups. When the PMF is routed, the rising and falling limbs of the outflow hydrograph make 10 sudden jumps and sudden drops at definite times and smoothly vary between steps. The developed flood routing model is applied sequentially to Catalan and Seyhan dams on the Seyhan River in Turkey

Comparison of three-back-propagation training algorithms for two case studies

This paper investigates the use of three back-propagation training algorithms, Levenberg-Marquardt, conjugate gradient and resilient back-propagation, for the two case studies, stream-flow forecasting and determination of lateral stress in cohesionless soils. Several neural network (NN) algorithms have been reported in the literature. They include various representations and architectures and therefore are suitable for different applications. In the present study, three NN algorithms are compared according to their convergence velocities in training and performances in testing. Based on the study and test results, although the Levenberg-Marquardt algorithm has been found being faster and having better performance than the other algorithms in training, the resilient back-propagation algorithm has the best accuracy in testing period

Reviewed Work: Evolution as Entropy: Toward a Unified Theory of Biology

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