Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatic Methods and Machine Learning

Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) understanding of event sediment dynamics, and (3) prediction of sediment loading using meteorological data as inputs. Streambank movement is an integral part of geomorphic changes along river corridors and also a significant source of fine sediment to receiving waters. Advances in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for rapid and economical quantification of streambank erosion and deposition at variable scales. We assess the performance of UAS-based photogrammetry to capture streambank topography and quantify bank movement. UAS data were compared to terrestrial laser scanner (TLS) and GPS surveying from Vermont streambank sites that featured a variety of bank conditions and vegetation. Cross-sectional analysis of UAS and TLS data revealed that the UAS reliably captured the bank surface and was able to quantify the net change in bank area where movement occurred. Although it was necessary to consider overhanging bank profiles and vegetation, UAS-based photogrammetry showed significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. This study also used a new machine-learning tool to improve the analysis of sediment dynamics using three years of high-resolution suspended sediment data collected in the Mad River watershed. A restricted Boltzmann machine (RBM), a type of artificial neural network (ANN), was used to classify individual storm events based on the visual hysteresis patterns present in the suspended sediment-discharge data. The work expanded the classification scheme typically used for hysteresis analysis. The results provided insights into the connectivity and sources of sediment within the Mad River watershed and its tributaries. A recurrent counterpropagation network (rCPN) was also developed to predict suspended sediment discharge at ungauged locations using only local meteorological data as inputs. The rCPN captured the nonlinear relationships between meteorological data and suspended sediment discharge, and outperformed the traditional sediment rating curve approach. The combination of machine-learning tools for analyzing storm-event dynamics and estimating loading at ungauged locations in a river network provides a robust method for estimating sediment production from catchments that informs watershed management

Assessment of climate change and development of data based prediction models of sediment yields in Upper Indus Basin

Hohe Raten von Sedimentflüssen und ihre Schätzungen in Flusseinzugsgebieten erfordern die Auswahl effizienter Quantifizierungsansätze mit einem besseren Verständnis der dominierten Faktoren, die den Erosionsprozess auf zeitlicher und räumlicher Ebene steuern. Die vorherige Bewertung von Einflussfaktoren wie Abflussvariation, Klima, Landschaft und Fließprozess ist hilfreich, um den geeigneten Modellierungsansatz zur Quantifizierung der Sedimenterträge zu entwickeln. Einer der schwächsten Aspekte bei der Quantifizierung der Sedimentfracht ist die Verwendung traditioneller Beziehung zwischen Strömungsgeschwindigkeit und Bodensatzlöschung (SRC), bei denen die hydrometeorologischen Schwankungen, Abflusserzeugungsprozesse wie Schneedecke, Schneeschmelzen, Eisschmelzen usw. nicht berücksichtigt werden können. In vielen Fällen führt die empirische Q-SSC Beziehung daher zu ungenauen Prognosen. Heute können datenbasierte Modelle mit künstlicher Intelligenz die Sedimentfracht präziser abschätzen. Die datenbasierten Modelle lernen aus den eingespeisten Datensätzen, indem sie bei komplexen Phänomenen wie dem Sedimenttransport die geeignete funktionale Beziehung zwischen dem Output und seinen Input-Variablen herstellen. In diesem Zusammenhang wurden die datenbasierten Modellierungsalgorithmen in der vorliegenden Forschungsarbeit am Lehrstuhl für Wasser- und Flussgebietsmanagement des Karlsruher Instituts für Technologie in Karlsruhe entwickelt, die zur Vorhersage von Sedimenten in oberen unteren Einzugsgebieten des oberen Indusbeckens von Pakistan (UIB) verwendet wurden. Die dieser Arbeit zugrunde liegende Methodik gliedert sich in vier Bearbeitungsschritte: (1) Vergleichende Bewertung der räumlichen Variabilität und der Trends von Abflüssen und Sedimentfrachten unter dem Einfluss des Klimawandels im oberen Indus-Becken (2) Anwendung von Soft-Computing-Modellen mit Eingabevektoren der schneedeckten Fläche zusätzlich zu hydro-klimatischen Daten zur Vorhersage der Sedimentfracht (3) Vorhersage der Sedimentfracht unter Verwendung der NDVI-Datensätze (Hydroclimate and Normalized Difference Vegetation Index) mit Soft-Computing-Modellen (4) Klimasignalisierung bei suspendierten Sedimentausträge aus Gletscher und Schnee dominierten Teileinzugsgebeiten im oberen Indus-Becken (UIB). Diese im UIB durchgeführte Analyse hat es ermöglicht, die dominiertenden Parameter wie Schneedecke und hydrologischen Prozesses besser zu und in eine verbesserte Prognose der Sedimentfrachten einfließen zu lassen. Die Analyse der Bewertung des Klimawandels von Flüssen und Sedimenten in schnee- und gletscherdominierten UIB von 13 Messstationen zeigt, dass sich die jährlichen Flüsse und suspendierten Sedimente am Hauptindus in Besham Qila stromaufwärts des Tarbela-Reservoirs im ausgeglichenen Zustand befinden. Jedoch, die jährlichen Konzentrationen suspendierter Sedimente (SSC) wurden signifikant gesenkt und lagen zwischen 18,56% und 28,20% pro Jahrzehnt in Gilgit an der Alam Bridge (von Schnee und Gletschern dominiertes Becken), Indus in Kachura und Brandu in Daggar (von weniger Niederschlag dominiertes Becken). Während der Sommerperiode war der SSC signifikant reduziert und lag zwischen 18,63% und 27,79% pro Jahrzehnt, zusammen mit den Flüssen in den Regionen Hindukush und West-Karakorum aufgrund von Anomalien des Klimawandels und im unteren Unterbecken mit Regen aufgrund der Niederschlagsreduzierung. Die SSC während der Wintersaison waren jedoch aufgrund der signifikanten Erwärmung der durchschnittlichen Lufttemperatur signifikant erhöht und lagen zwischen 20,08% und 40,72% pro Jahrzehnt. Die datenbasierte Modellierung im schnee und gletscherdominierten Gilgit Teilbecken unter Verwendung eines künstlichen neuronalen Netzwerks (ANN), eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit Gitterpartition (ANFIS-GP) und eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit subtraktivem Clustering (ANFIS) -SC), ein adaptives Neuro-Fuzzy-Logik- Inferenzsystem mit Fuzzy-C-Mittel-Clustering, multiplen adaptiven Regressionssplines (MARS) und Sedimentbewertungskurven (SRC) durchgeführt. Die Ergebnisse von Algorithmen für maschinelles Lernen zeigen, dass die Eingabekombination aus täglichen Abflüssen (Qt), Schneedeckenfläche (SCAt), Temperatur (Tt-1) und Evapotranspiration (Evapt-1) die Leistung der Sedimentvorhersagemodelle verbesserne. Nach dem Vergleich der Gesamtleistung der Modelle schnitt das ANN-Modell besser ab als die übrigen Modelle. Bei der Vorhersage der Sedimentfrachten in Spitzenzeiten lag die Vorhersage der ANN-, ANIS-FCM- und MARS-Modelle näher an den gemessenen Sedimentbelastungen. Das ANIS-FCM-Modell mit einem absoluten Gesamtfehler von 81,31% schnitt bei der Vorhersage der Spitzensedimente besser ab als ANN und MARS mit einem absoluten Gesamtfehler von 80,17% bzw. 80,16%. Die datenbasierte Modellierung der Sedimentfrachten im von Regen dominierten Brandu-Teilbecken wurde unter Verwendung von Datensätzen für Hydroklima und biophysikalische Eingaben durchgeführt, die aus Strömungen, Niederschlag, mittlerer Lufttemperatur und normalisiertem Differenzvegetationsindex (NDVI) bestehen. Die Ergebnisse von vier ANNs (Artificial Neural Networks) und drei ANFIS-Algorithmen (Adaptive Neuro-Fuzzy Logic Inference System) für das Brandu Teilnbecken haben gezeigt, dass der mittels Fernerkundung bestimmte NDVI als biophysikalische Parameter zusätzlich zu den Hydroklima-Parametern die Leistung das Modell nicht verbessert. Der ANFIS-GP schnitt in der Testphase besser ab als andere Modelle mit einer Eingangskombination aus Durchfluss und Niederschlag. ANN, eingebettet in Levenberg-Marquardt (ANN-LM) für den Zeitraum 1981-2010, schnitt jedoch am besten mit Eingabekombinationen aus Strömungen, Niederschlag und mittleren Lufttemperaturen ab. Die Ergebnisgenauigkeit R2 unter Verwendung des ANN-LM-Algorithmus verbesserte sich im Vergleich zur Sedimentbewertungskurve (SRC) um bis zu 28%. Es wurde gezeigt, dass für den unteren Teil der UIB-Flüsse Niederschlag und mittlere Lufttemperatur dominierende Faktoren für die Vorhersage von Sedimenterträgen sind und biophysikalische Parameter (NDVI) eine untergeordnete Rolle spielen. Die Modellierung zur Bewertung der Änderungen des SSC in schnee- und gletschergespeiste Gilgit- und Astore-Teilbecken wurde unter Verwendung des Temp-Index degree day modell durchgeführt. Die Ergebnisse des Mann-Kendall-Trendtests in den Flüssen Gilgit und Astore zeigten, dass der Anstieg des SSC während der Wintersaison auf die Erwärmung der mittleren Lufttemperatur, die Zunahme der Winterniederschläge und die Zunahme der Schneeschmelzen im Winter zurückzuführen ist. Während der Frühjahrssaison haben die Niederschlags- und Schneedeckenanteile im Gilgit-Unterbecken zugenommen, im Gegensatz zu seiner Verringerung im Astore-Unterbecken. Im Gilgit-Unterbecken war der SSC im Sommer aufgrund des kombinierten Effekts der Karakorum-Klimaanomalie und der vergrößerten Schneedecke signifikant reduziert. Die Reduzierung des Sommer-SSC im Gilgit Fluss ist auf die Abkühlung der Sommertemperatur und die Bedeckung der exponierten proglazialen Landschaft zurückzuführen, die auf erhöhten Schnee, verringerte Trümmerflüsse Trümmerflüsse und verringerte Schneeschmelzen von Trümmergletschern zurückzuführen sind. Im Gegensatz zum Gilgit River sind die SSC im Astore River im Sommer erhöht. Der Anstieg des SSC im Astore-Unterbecken ist auf die Verringerung des Frühlingsniederschlags und der Schneedecke, die Erwärmung der mittleren Sommerlufttemperatur und den Anstieg des effektiven Niederschlags zurückzuführen. Die Ergebnisse zeigen ferner eine Verschiebung der Dominanz von Gletscherschmelzen zu Schneeschmelzen im Gilgit-Unterbecken und von Schnee zu Niederschlägen im Astore-Unterbecken bei Sedimenteden Sedimentfrachten in UIB. Die vorliegende Forschungsarbeit zur Bewertung der klimabedingten Veränderungen des SSC und seiner Vorhersage sowohl in den oberen als auch in den unteren Teilbecken des UIB wird nützlich sein, um den Sedimenttransportprozess besser zu verstehen und aufbauen auf dem verbessertenProzessverständnis ein angepasstes Sedimentmanagement und angepasste Planungen der zukünftigen Wasserinfrastrukturen im UIB ableiten zu können

Application of soft computing models with input vectors of snow cover area in addition to hydro-climatic data to predict the sediment loads

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R$^{2}$ value of 0.85 and 0.74 during the training and testing period, respectively

The need for operational reasoning in data-driven rating curve prediction of suspended sediment

The use of data-driven modelling techniques to deliver improved suspended sediment rating curves has received considerable interest in recent years. Studies indicate an increased level of performance over traditional approaches when such techniques are adopted. However, closer scrutiny reveals that, unlike their traditional counterparts, data-driven solutions commonly include lagged sediment data as model inputs and this seriously limits their operational application. In this paper we argue the need for a greater degree of operational reasoning underpinning data-driven rating curve solutions and demonstrate how incorrect conclusions about the performance of a data-driven modelling technique can be reached when the model solution is based upon operationally-invalid input combinations. We exemplify the problem through the re-analysis and augmentation of a recent and typical published study which uses gene expression programming to model the rating curve. We compare and contrast the previously-published, solutions, whose inputs negate their operational application, with a range of newly developed and directly comparable traditional and data-driven solutions which do have operational value. Results clearly demonstrate that the performance benefits of the published gene expression programming solutions are dependent on the inclusion of operationally-limiting, lagged data inputs. Indeed, when operationally inapplicable input combinations are discounted from the models, and the analysis is repeated, gene expression programming fails to perform as well as many simpler, more standard multiple linear regression, piecewise linear regression and neural network counterparts. The potential for overstatement of the benefits of the data-driven paradigm in rating curve studies is thus highlighted

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection

DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empirical models. This paper demonstrates how hydrological insight and knowledge of data quality issues can be better incorporated into the sediment-discharge data-driven model assessment procedure: by the plotting of datasets and modelled relationships; and from an understanding and appreciation of the hydrological context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrological soundness of data-driven single-input single-output sediment rating curve solutions is presented. The approach is adopted and exemplified in an evaluation of seven explicit sediment-discharge models that are used to predict daily suspended sediment concentration values for a small tropical catchment on the island of Puerto Rico. Four neurocomputing counterparts are compared and contrasted against a set of traditional log-log linear sediment rating curve solutions and a simple linear regression model. The statistical assessment procedure provides one indication of the best model, whilst graphical and hydrological interpretation of the depicted datasets and models challenge this overly-simplistic interpretation. Traditional log-log sediment rating curves, in terms of soundness and robustness, are found to deliver a superior overall product — irrespective of their poorer global goodness-of-fit statistics

Evaluation Of Artificial Neural Network (ann) And Adaptive Neuro Based Fuzzy Inference System (anfis) On Sediment Transport

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2012Hidrolik ve Su Kaynakları Mühendisliğinde Sediment Taşınımının Öneminine Ayrıntılı Bir Şekilde Bakmak Zorunludur ve Bu Çok Büyük Öneme Sahiptir. Her Zaman, Bu Alanın Uzmanları ve Bilim İnsanları İçin Sediment ve Taşınımı Önemli Bir Mesele Haline Geldi. Mesela, 1950’lerden Beri Sediment Taşınımın Davranışını Değerledirmek İçin Çok Çeşitli Çalışmalar Laboratuvarlarda Yürütülmekteydi. Akarsular havzalarından gelen ya da yataklarından söktükleri sediment tanelerini taşırlar. Su ile katı tanelerin birlikte hareket ettikleri iki fazlı akımın hidroliği ve taşınan sediment miktarının belirlenmesi mühendislik açısından büyük önem taşıdığı kadar, incelenmesi çok güç olan problemlerdir. Akarsuların düzenlenmesi, çeşitli maksatlarla kullanılması ve akarsulardan su alma ile ilgili mühendislik problemlerine başarılı çözümler bulabilmek için akarsularda akım ve sediment taşınımı konusunda yeterli bilgilere sahip olmak gerekir. Yüzeysel erozyon, tortu taşınması ve birikmesi, ekonomik ve kültürel gelişimde önem arz etmesi nedeniyle asırlarca jeoloji mühendislerinin araştırma konusu olmuştur. Eski medeniyetler tarafından su kaynakları ve akarsular tarımda ve ulaşım alanlarında kullanılmıştır. Bütün akarsular hem su kaynaklarındaki yüzeysel erozyon hem de kitlesel olarak akarsu kenarlarındaki potansiyel erozyon alanları nedeniyle tortu taşınmasını içlerinde barındırmaktadır. Bizim anlayışımıza göre aşınmanın optimum dengesi konusu membadadır; akarsuyun erozyon taşıma kapasitesi tasarım, yararlanma, onarım ve koruma konusunda önem arz etmektedir. Seddeler akarsu kenarlarında taşkın kontrolü için yapılmaktadır. Ayrıca bu seddeler nedeniyle güvenilir bir şekilde su kaynağı oluşturabilmek için depoların yapılması gereklidir.Kanallar su taşıma ve elde etmek için yapılırlar. Kalıcı olarak bu hidrolik yapıların kullanılması bizim anlayışımıza göre erozyon, tortu süreci ve onları hidrolik projelerde nasıl birleştirebileceğimizle alakalıdır. Artan bulanıklık, su bitkilerin büyümesine sebep olur. Siltin suda olması ışığın girmesine ve sonuç oalrak su bitiklerinin fotosentez ve büyümelerine engel olur. Depolanan sedımentler su altında veya nehir üzerinde olan bitikleri boga bilir. Tarım, bazı sanayi süreçler ve kanalizasyondan gelen sediment ler nitrat ve fosfat oranını arta biler ve sonuç da sedimentin yukselmesine sebep olabilirler. Sediment yönetimi, özellikle sediment hareketinin kontrolü, oyulma-birikme, nehir mühendisliğinde karşılaşılan en zor problemlerden biridir. Nehir yatağındaki hız ve derinliğin zamanla değişmesinin yanı sıra su alma yapısına giren akım miktarı da zamanla değişebilir. Nehir kıyılarında güç santrallerinin soğutma suyu, endüstri su ihtiyacı, sulama vb. Amaçları karşılamak için kullanılan su alma yapılarının etrafı sık sık sediment birikimi dolayısıyla kuşatılır. Bu sebeple nehir tesislerindeki su alma yapılarında aşınma ve birikme problemleri göz önünde bulundurulmalı ve sediment girişini minimum tutacak şekilde tasarlanmalıdır. Akım ve sediment ile ilgili değişkenlerdeki belirsizlikler sebebiyle oyulma ve birikme hakkında kesin bir yargıya varılamamıştır. Bu sebeple sediment kontrol yapılarının tasarımı ve sıralanışı optimum çözümün elde edilebilmesi için fiziksel model çalışmalarına dayandırılmalıdır. Bu ihtiyaç özellikle üç boyutlu akımın olduğu su alma yapıları civarında ortaya çıkmaktadır. Kıvrımlı nehirlerin dış şevindeki yatak oyulması şevlerin zayıflamasına ve toprak kaybına sebep olur. Sediment birikimi nehrin akım taşıma kapasitesini düşürür ve ulaşım için faydalanılan nehirlerde gemi ulaşımına engel olur. Çoklu değişkenler sedimentin doğasına ve akım hidroliğine etki etmekteler. Diğer taraftan tortu taşınması çok karmaşık bir konudur ve genel olarak teorik veya yarı teorik bir şekilde araştırılır. Genel olarak araştırmalar teorik olarak bazı basit tahminlere dayandırılır ve ideal olarak dikkate alınması gereken suyun debisi, akım ortalama hızı, enerji eğimi ve kayma gerilmesi gibi önemli etkenlerden bir veya iki tanesi seçilerek belirlenir. Bilim adamları sayesinde bir takım formüller elde edilmiştir ve bu konu gün geçtikçe gelişmektedir. Bazen bilimadamları birbirlerininkine yakın sonuçlar elde etmektedirler ve bazen de zıtlıklar oluşmaktadır. Sonuç olarak bilim adamları bu konuda evrensel olarak anlaşmaya varamamışlardır. Öte yandan günümüzde teknolojinin gelişmesiyle ve bilgisayarın kullanımıyla Yapay Sınır Ağları (YSA) ve ANFIS gibi bilgisayar programlarının ortaya çıkmasıyla tortu taşınması konusunda güvenilirliği yüksek formüller çeşitli bilimadamları tarafından elde edilmiştir. Bugünlerde MATLAB gibi Bilgisayar Destekli Programların Gelişimi Araştırmacılar İçin Hesaplamaları Hızlı ve Etkin Bir Biçimde Yapmanın Yolunu Açtı. Sediment Taşınımında, Yapay Sinir Ağları (YSA) ve ANFIS Laboratuvar Verisini Yada Gerçek Bir Nehrin Verisini Değerlendirmek İçin Yoğun Bir Şekilde Kullanıldı. Yang (1983) Araştırmaları Diyagramlar Halinde Sunulmuştur. Bahsi Geçen Diyagramlar Su Akımı, Ortalama Hız, Su Yüzey Eğimi, Kayma Gerilimi, Akış Gücü ve Toplam Sediment Akımlı (TSA) Birim Akış Gücü Arasındaki İlişkiler Hakkında. Giriş Veri Değerlerini Elde Etmek İçin Get Data Graph Digitizer Programı Kullanıldı. Ayrıca, 79 Veri Kümesi Nitelendirilmiştir. Her Biri İçin, Duşey Değerlerinin Ortalaması Hesaplanmış ve Değerlendirme İçin Gözlemlenmiş Çıkış Verisi Olarak Kullanılmıştır. ANN’in İleri Geri Beslemeli Yayılım (İGBY) Türünden, ANFIS’in Sugeno Türüne Dayanan Geri Yayılım (GY) Türlerinden İki Sınıfta Deneme ve Test Olarak Veri Analizinde ve Sonuçlar Vermede Faydalanıldı. Layerların sayılarını 2 ile 4 arası ve nöronların sayılarını 1 ile 4 arası (İGBY)’ye dayanarak genel alternatif senaryolar geliştirerek TSD’yi tahmin etmeye yardımcı oluyor.İlerleme sırasında hataların tipi RMSE ve korelasyonları elde etmede bizim için önemlidir. Böylece TSD modellemesi için en iyi ve en optimum alternatif Yapay Sınır Ağlarının İGBY’ye dayanarak iki gizli layerlı ve her bir layerı iki nöron sayılı bir kombinasyon ile 0.99 R2 ve 0.017 RMSE olacak şekilde öneriliyor. TSD’yi tahmin ederken R2 için yaklaşık 1 değeri ve çok küçük RMSE değeri (<0.04) bu metodun yüksek kapasitesini göstermektedir. Öte yandan ANFIS programıyla girdi üyelik fonksiyonu olarak, Gauss ve Gauss 2; çıktı üyelik fonksiyonu olarak sabit ve lineer tipler kullanıldı. Sonuç olarak ANFIS programıyla hibrit ve BP metotlarına odaklanırken genel kapsamlı TSD tahmin metodolojileri kullanıldı.TSD’yi tahmin etmek için gösterildiği gibi çok büyük R2 değerleri ve çok küçük RMSE değerlerine dayanarak hibrit ve BP metodlarının yüksek kapasitesi sağlanmaktadır. Daha Sonra, Tahmin Edilen ve Gözlenen Değerler Arasındaki İlişki Diyagramlar Halinde Gösterildi. Yapılan Çalışmada 0.99’dan Daha Yüksek Tespit Katsayısı (R2) Bağıntısı ANN ve ANFIS’in Toplam Sediment Akımını Tahmin Etmek İçin Uygunluğunu ve Yeterliliğini Kanıtlamıştır.With regard to the importance of sediment transportation in Hydraulic and Water Resources Engineering, it is essential to focus on the topic with details and it is a matter of paramount importance. Recently, sediment and its transportation have become an important issue to experts and scientists. Since 1950s, a wide variety of studies have been conducted in laboratories to evaluate the behavior of sediment transportation. Nowadays, improvement of the computer-aided programs such as MATLAB has paved the way for researchers to explore the generation mechanism easily. In sediment transportation, Artificial Neural Networks (ANN) and ANFIS may be intensely used for evaluation of the laboratory data or a definite river’s data. In this study, researches of Yang (1983) have been offered, which are about relationships between water discharge, average velocity, water surface slope, shear stress, stream power and unit stream power with total sediment discharge (TSD). The parameter of unit stream power has been neglected due to the fact that it is very similar to th repetitive manner of other parameters. For getting the input data values, Get Data Graph Digitizer software has been used, where 79 set of data has been considered. For each one, the mean of their output values have been extracted and used as observed output data for evaluation. Feed Forward Back Propagation (FFBP) type of ANN and Hybrid, Back Propagation (BP) types based on Sugeno’s approach of ANFIS have been utilized in analyzing the data and giving the results in two classifications as training and testing stages. Subsequently, the relationship between predicted and observed values have been obtained in the forms of scatter diagrams. Correlation (R2) of higher than 0.99 proves the compatibility and capability of ANN and ANFIS for predicting total sediment discharge. &#8195;Yüksek LisansM.Sc

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg/day) in Jenderan catchment area