Discussion of “clustering on dissimilarity Representations for detecting mislabelled Seismic signals at Nevado del Ruiz Volcano” by Mauricio Orozco-Alzate, and César Germán Castellanos-Domínguez

The authors are to be congratulated for a systematic investigationof the accurate and non subjective classifying approach in seismic research. The authors have conducted several clustering algorithms to the seismic event records from Volcanological and SeismologicalObservatory at Manizales. Their objective was to improve the grouping of seismic data (i.e., volcano-tectonic earthquakes, long-period earthquakes and icequakes) digitized at 100.16 Hz sampling frequency.Their study seems adding new approach to their previous work of Langer et al. (2006) who applied different classification techniques to seismic data

Hydrologic homogeneous regions using monthly Streamflow in Turkey

Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of objectively classifying streamflow data into regions encompassing similar streamflow patterns over Turkey. The performance of three standardization techniques was also tested, and standardizing by range was found better than standardizing with zero mean and unit variance. Clustering was carried out using Ward’s minimum variance method which became prominent in managing water resources with squared Euclidean dissimilarity measures on 80 streamflow stations. The stations have natural flow regimes where no intensive river regulation had occurred. A general conclusion drawn is that the zones having similar streamflow pattern were not be overlapped well with the conventional climate zones of Turkey; however, they are coherent with the climate zones of Turkey recently redefined by the cluster analysis to total precipitation data as well as homogenous streamflow zones of Turkey determined by the rotated principal component analysis. The regional streamflow information in this study can significantly improve the accuracy of flow predictions in ungauged watersheds

Streamflow and La Niña event relationships in the ENSO-streamflow core areas

EXTRACT (SEE PDF FOR FULL ABSTRACT): The high index phase of the Southern Oscillation (SO), La Niña, has not been given as much attention as its counterpart, the low index phase of the SO, El Niño. One reason may be related to the fact that many similarities exist among El Niño events but not among La Niña events. ... In this study, we focus on the influences of La Niña phenomena on streamflow anomalies ... to explore the SO-related signal over the United States

KONYA HAVZASI AKARSULARI YILLIK PİK AKIM SERİLERİNİN TAŞKIN FREKANS ANALİZİ

İki bölümden oluşanın birinci bölümünde, taşkın frekans analizi hesaplamalarının temel varsayım olarak kabul ettiği bağımsızlık tezinin varlığı incelenmiştir. Bu amaçla; otokorelasyon, medyanı çaprazlama, dönüm noktaları, sıra farklılık ve Spearman sıralı seri korelasyon katsayısı bağımlılık testleri Konya Havzası’nda bulunan 13 akarsuya ait yıllık pik akım serilerine uygulanmıştır. Uygulanan testlerin en az ikisine göre 13 akarsuyun sadece bir tanesi bağımlı bir karakter göstermiştir. Gerçekleştirilen taban akımı analizi sonucunda, yıllık pik akımların birinci seri otokorelasyon katsayıları ile havzadaki su tutma kapasitesi arasında dikkat çekici bir ilişki olmadığı ve bağımsızlık tezinin havzada bulunan akarsular için geçerli olduğu sonucuna varılmıştır. Çalışmanın ikinci bölümünde, 12 istasyona ait yıllık pik akım serilerine iki ve üç parametreli log-normal,Gumbel, Pearson-3, log-Pearson-3, Log-Boughton, log-logistic, ekstrem değerler dağılımları uygulanarak en uygun olasılık dağılım modelinin belirlenmesine çalışılmıştır. Modellerin en uygununu belirlemek amacıyla, klasik uygunluk testleri, khi-kare ve Kolmogorov-Smirnov testleri kullanılmıştır. Bu testlerin değerlendirmelerine göre, Log-Pearson -3’ün diğerlerine göre daha uygun bir model olduğu sonucuna varılmıştır

Türkiye göl su seviyelerinin eğilim ve harmonik analizi

 Lake levels have been continuously affected by hydrological, meteorological and anthropogenic conditions. Hydroclimatologic variables are important indicators of climatic change. It will be of great use to know the regime and tendency of changes in lake water levels in Turkey for both current and future studies, which are of importance for accurate assumption in future. Therefore, annual Turkish lake levels are analyzed to document some of their variability characteristics in this study. Annual cycles are first computed from monthly means at each individual lake station. The amplitudes and phases of the first harmonics, fitted to monthly lake level means, are displayed in vectorial fashion. Secondly a non-parametric trend test is applied to detect possible linear trends in the lake levels. The Mann-Kendall is known as appropriate tool in detecting linear trends of a hydrological time series. Statistically significant upward trends in lake level were found at the larger scale in the north coastal region. Downward trends in lake level were found at the larger scale the Midwest region. These results are in agreement with those of the precipitation, streamflow and temperature trend studies in Turkey. Harmonically significant maximum water level of spring season were found at large scale in the northwest regions and southwest region while summer season in lake level were found at the east regions. Keywords: Annual cycle, climate change, lake level, trend analysis, the first harmonic. 20. yüzyılın başından beri dünyanın pek çok yerinde göl seviyelerinde ölçümler yapılmaktadır, ölçülen göl su seviyelerinde alçalmalar ve yükselmeler gözlenmektedir. Göl su seviyeleri hidrolojik, meteorolojik ve antropojenik şartlardan etkilenirler. Çalışmada, tüm Türkiye’yi temsil edebilecek 25 göl su seviye verilerinin zamana göre eğilimleri parametrik olmayan Mann-Kendall testi ile incelenirken, göl su seviyelerinin mevsimsel değişkenliği ve bölgesel değişimi de harmonik analizle incelenmiştir. Bulunan sonuçlar Türkiye Yağış Rejimleriyle ilişkilendirilmiştir. Bu araştırmanın sonuçlarının, şimdiki ve ilerideki bölgesel gelişme çalışmalarında yararlı olacağı düşünülmektedir. Bu çalışmada amaç göl su seviyelerinde mevsimsel değişkenliğin coğrafik ölçeğinin saptanması ve Türkiye genelinde göl rejimi hakkında bir fikir sahibi olmaktır.Anahtar Kelimeler: Yıllık döngü, iklim değişimi,göl su seviyesi, eğilim analizi, birinci harmonik

ENSO effects on mean temperature in Turkey

Abstract. The ENSO effects on Turkish streamflow and precipitation patterns were previously analyzed by applying the t-test on eight standard seasons beginning with the JJA (-1) season of the year before the event year and ending with the MAM (+1) season of the year after the event year. The objective of this study is to identify the ENSO effects on the mean temperature data in Turkey by using the same methodology used for streamflow and precipitation. The methodology mainly comprises of two phases: first, composite analysis; and second, statistical t-test analysis. An overall result shown by this study is that the response of temperature to ENSO events was not much noticeable than those of the two hydroclimatological variables. Any positive anomaly could not be detected during the classical seasons of the event year, indicating that the mean temperature values occur below the average. The dominancy of cold anomaly conditions begins with the JJA (-1) season and continues until the DJF (+1) season. Furthermore the MAM (0) season has a maximum number of negative anomalies when compared to other cold anomaly seasons. Besides the positive anomaly conditions of streamflow and precipitation at the event year, the temperature values exhibited negative anomaly conditions at the same time period. In this study we aimed to determine whether there exists any relationship between temperature, streamflow and precipitation patterns of Turkey in terms of responding to the ENSO forcing. In conclusion, a sign of the tropical biennial cycle was, to some extent, evident surface climate variables

Estimation of precipitation data using artificial neural networks and wavelet transform

Meteorolojik değişkenlerden birisi olan yağış, su kaynakları açısından çok önemlidir. Yağış, akışı meydana getiren en önemli değişkendir. Kısa süreli aşırı yağışlar önemli taşkınlara neden olmaktadır. Uzun sürelerde yeterli yağışın meydana gelmemesi durumunda kuraklık meydana gelmektedir. Karmaşık bir fiziksel süreç sonucunda meydana gelen yağışın doğru tahmin edilmesi genellikle zordur. Özellikle yersel değişimlerden ve bölgesel özelliklerden oldukça etkilenmesi yağışın tahminini daha zorlaştırmaktadır. Lineer olmayan sistemlerin davranışında başarıyla kullanılabilen bir kara-kutu modeli olan yapay sinir ağları, böylesine karmaşık değişkenlerin tahmininde başarıyla kullanılmaktadır. Yapay sinir ağlarında tahminin başarısı üzerinde kullanıcının etkileri sınırlıdır ve daha çok girdilere bağlıdır. Yapay sinir ağları yönteminin tahmindeki başarısını arttırmak için dalgacık dönüşümü bu çalışmada kullanılmıştır. Dalgacık dönüşümü, verilerin hem zaman hem de frekans ortamında incelenmesine olanak sağlayan bir yöntemdir. Bu çalışmada Yapay Sinir Ağları (YSA) ve dalgacık dönüşümü yöntemleri ile günlük yağış tahmini yapılmıştır. Bu amaçla Türkiye’ye ait 3 istasyonun günlük meteorolojik verileri kullanılmıştır. YSA yönteminin literatürde en çok kullanılan algoritmalarından, İleri Beslemeli Geriye Yayılmalı Yapay Sinir Ağları (İBGYSA) ve Radyal Tabanlı Yapay Sinir Ağları (RTYSA) yöntemleri yağış tahmini amacıyla kullanılmıştır. Farklı girdi kombinasyonları denenerek her istasyon için en uygun model bulunmaya çalışılmıştır. Sonuçlarda ileri beslemeli geriye yayılmalı yapay sinir ağları algoritmasının kullanıldığı yöntem en iyi performansı göstermiştir. Dalgacık dönüşümü-YSA yönteminin tahmin sonuçları çoklu lineer regresyon yönteminin sonuçları ile kıyaslanmış ve performans kriterlerine göre daha iyi olduğu bulunmuştur.  Anahtar Kelimeler: Yapay Sinir Ağları, dalgacık dönüşümü, tahmin, yağış.Forecasting the precipitation which is one of the most important meteorological variables is very important for planning and management of the water resources. Accurate precipitation prediction is one of the most difficult tasks in the meteorology because the complex physical processes involved and the variability of the precipitation is highly dependent on small scale processes and local geography. Especially the daily precipitation forecasting is one of the most challenging works and very important for the flood and drought analyses. Artificial Neural Networks (ANN) are a useful tool to identify this relation. ANN approach is extensively used in the water resources literature in recently years. Artificial neural networks which is a black-box model, is used successfully in the modeling of non-stationary and complex variables. Black-box models are divided generally as linear and nonlinear and in particular artificial neural networks method is used in the modeling of nonlinear system behavior. The artificial neural networks have some advantages, such as easily applied, not needing much data. However the accuracy of model predictions is very subjective and highly dependent on user's ability, knowledge and understanding of the model. Especially, the input selecting is one of the most important phases in any ANN modeling study Because of this, wavelet transformation is used for increase of user ability and success of artificial neural networks. The wavelet transform, which can produce a good local representation of the signal in both the time and frequency domains, provides considerable information about the structure of the physical process to be modeled and has positive effects on the ANN modeling ability. Because of these reasons, coupling wavelets with the ANN can provide significant advantages on predicting. This study aims to predict the daily precipitation data of three belong to Turkish meteorological stations by applying the ANN methods and discrete wavelet transform. For this reason, the original time series were decomposed into a certain number of sub-time series using the wavelet transform. Then, the suitable sub time series constituted the inputs of the ANN and the resulting model was applied to forecast the original time series. The sub time series decomposed by discrete wavelet transform from the original time series provide detailed information about the data structure and its periodicity. Behavior of each sub-series is different. For the selection of dominant sub-series, the correlation coefficients between the decomposed wavelet sub-series and the observed precipitation time series are computed. The selection of dominant sub-series becomes effective on the output data improving ANN model's performance. In this study, the wavelet transforms and the ANN has been applied to estimate the daily precipitation. The meteorological data belong to the three station were investigated for this study. These are the daily mean temperature, the daily maximum temperature, the daily minimum temperature, the daily total specific humidity, the daily total evaporation and the daily total precipitation. Each of the meteorological data considered as input for the ANN model was decomposed into the wavelet sub-series by Discrete Wavelet Transform (DWT). Then, ANN configuration is constructed with appropriate wavelet sub series as input and the original precipitation time series as output. So, different wavelet-ANN models were prepared for each station. Precipitation estimation was applied with the two different algorithms of the artificial neural networks. Employment of the Feed Forward Back Propagation (FFBP) in the precipitation estimation is compared with the Radial Basis Function (RBF) performances. The results were also compared with linear regression model. As a result, it was seen that the wavelet-feed forward back propagation method provided the best estimation performance. Results indicate that the wavelet-ANN model estimations were superior to the ones obtained by the multi linear regression model. The wavelet-ANN models have provided a good fit with the observed data, especially for the time series which have zero precipitation in the summer months. It was seen that the ANN-wavelet method provided very successful estimation performance. This study is the first application to the daily precipitation estimations using the wavelet sub-series of the various meteorological variables in the water resources literature. The wavelet-ANN method is especially convenient in variables having non-linear dynamics such as predicting of precipitation data.  Keywords: Artificial Neural Networks, wavelet, precipitation

Enso modulations on streamflow characteristics

El Niño Southern Oscillation (ENSO) has been linked to climate and hydrologic anomalies throughout the world. This paperpresents how ENSO modulates the basic statistical characteristics of streamflow time series that is assumed to be affected byENSO. For this we first considered hypothetical series that can be obtained from the original series at each station by assumingnon-occurrence of El Niño events in the past. Instead those data belonging to El Niño years were simulated by the RadialBased Artificial Neural Network (RBANN) method. Then we compared these data to the original series to see a significant differencewith respect to their basic statistical characteristics (i.e., variance, mean and autocorrelation parameters). Various statisticalhypothesis testing methods were used for four different scenarios. Consequently if there exist a significant difference,then it can be inferred that the ENSO events modulate the major statistical characteristics of streamflow series concerned. Theresults of this research were in good agreement with those of the previous studies

Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning

The barriers for the development of continuous monitoring of Suspended Sediment Concentration (SSC) in channels/rivers include costs and technological gaps but this paper shows that a solution is feasible by: (i) using readily available high-resolution images; (ii) transforming the images into image analytics to form a modelling dataset; and (iii) constructing predictive models by learning inherent correlation between observed SSC values and their image analytics. High-resolution images were taken of water containing a series of SSC values using an exploratory flume. Machine learning is processed by dividing the dataset into training and testing sets and the paper uses the following models: Generalized Linear Machine (GLM) and Distributed Random Forest (DRF). Results show that each model is capable of reliable predictions but the errors at higher SSC are not fully explained by modelling alone. Here we offer sufficient evidence for the feasibility of a continuous SSC monitoring capability in channels before the next phase of the study with the goal of producing practice guidelines