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Determining water level changes in lakes is significant for sustainable water supply planning, flood control, water resource management, and economic development with ecosystem sustainability. Trend analysis is a commonly used tool for detecting changes in hydrologic time series of quantities such as lake level, precipitation and temperature. Trend analysis of meteorological variables is very important for assessing the long-term changes in lake levels. This study examines long-term changes in the lakes in the Central Anatolia region of Turkey. Kizoren and Timras sinkhole lakes and Lakes Tuz and Beysehir are some of the lakes located in this region. Changes in these lakes were examined along with changes in precipitation trends and human effects. The precipitation stations representing these lakes were determined using Thiessen polygons. In this study, the well-known nonparametric Mann–Kendall trend test (MK) was used. Before performing trend analysis, homogeneity and autocorrelation tests were applied for the time series. The standard normal homogeneity test (SNHT) was used to evaluate homogeneity. Trend analysis was performed before and after the change points the inhomogeneous series for the period of the homogeneous series. In addition, the modified Mann–Kendall method (MMK) was performed to examine the strong autocorrelations seen in the entire time series. The results showed that precipitation station data are homogeneous and lake levels are inhomogeneous. Trend analysis was performed by determining the change points for the inhomogeneous lakes. There were no significant trends after the change points for the Lake Tuz and Timras sinkhole lake. These results are supported by the insignificant trends in precipitation stations. Contrary to the trend analysis results, the increase in the water level of Beysehir Lake was determined to increment from the water transferred to the lake. The decrease in the water level of Kizoren sinkhole was a result of anthropogenic effect rather than precipitation. The results confirm that lake levels have been affected by precipitation trends and anthropogenic effects.

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

This study investigates the applicability of three different soft computing methods, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS), and M5 Model Tree (M5-Tree), in forecasting SO2 concentration. These models were applied to monthly data obtained from Janakpuri, Nizamuddin, and Shahzadabad, located in Delhi, India. The models were compared with each other using the cross validation method with respect to root mean square error, mean absolute error, and correlation coefficient. According to the comparison, LSSVR provided better accuracy than the other models, while the MARS model was found to be the second best model in forecasting monthly SO2 concentration. Results indicated that the applied models gave better forecasting accuracy in Janakpuri station than the other stations. The results were also compared with previous studies and satisfactory results were obtained from three methods in modeling SO2 concentrations.

Streamflow forecasting and predicting are significant concern for several applications of water resources and management including flood management, determination of river water potentials, environmental flow analysis, and agriculture and hydro-power generation. Forecasting and predicting of monthly streamflows are investigated by using three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree). Data from four different stations, Besiri and Malabadi located in Turkey, Hit and Baghdad located in Iraq, are used in the analysis. Cross validation method is employed in the applications. In the first stage of the study, the heuristic regression models are compared with each other and multiple linear regression (MLR) in forecasting one month ahead streamflow of each station, individually. In the second stage, the models are evaluated and compared in predicting streamflow of one station using data of nearby station. The research investigated also the influence of the periodicity component (month number of the year) as an external sub-set in modeling long-term streamflow. In both stages, the comparison results indicate that the LSSVR model generally performs superior to the MARS, M5-Tree and MLR models. In addition, it is seen that adding periodicity as input to the models significantly increase their accuracy in forecasting and predicting monthly streamflows in both stages of the study.

Climate change impacts are perceived today as being very noticeable; determination of precipitation, wind, flow, evaporation and temperature trends has become essential for water resources and engineering projects in management and planning. Nowadays there is a lot of studies have been made in progress on global and regional climate changes in literature. In this study, monthly, annual and seasonal trends in average temperature, total precipitation, and average wind speed data calculated by Mann-Kendall, linear trend and Sen’s trend tests. The gauge station is located in Samsun, which is the largest city of the Turkey’s Black Sea region. The data sets are obtained for the period 1980 to 2015. According to the results, at the levels of significance of 5% and 10%, Mann-Kendall's statistical results were found to be generally similar to those of trend analysis methods.

This paper investigates the accuracy of three different adaptive neuro-fuzzy inference systems (ANFISs), ANFIS with grid partition (ANFIS-GP), ANFIS with substructive clustering (ANFIS-SC),...

Variations in temperature and precipitation have direct impacts on the physical, chemical and biological characteristics of the shallow lakes. This paper examines the possible linkages between climate variables and the water levels of shallow interconnected Lakes Mogan and Eymir, located 20 km south of Ankara in Central Anatolia. The variations in the lakes’ water levels during 1996–2015 are studied and the impacts of climate variables on the lake levels are assessed to address the long-term consequences. The nonparametric Mann–Kendall test was used to quantify trends in the climate variables and the lakes’ level fluctuations between the observation periods 1998–2007 and 2008–2014. Statistical analyses results showed that precipitation and temperature have crucial influence on the variations in the lakes’ levels. The projected increase in temperature and decrease in precipitation over the next century may produce substantial decreases in lake levels, with consequent drying of both lakes.

Bu araştırmada, Karadeniz’e dökülenakarsulardaki 52 adet akım gözlem istasyonuna ait Yıllık Anlık Maksimum Akım(YAMA) verileri kullanılarak L-Momentler yöntemiyle bölgesel frekans analiziyapılmıştır. Hosking tarafından sağlanan bilgisayar programı kullanılarakgerçekleştirilen uygulama beş büyük havzayı kapsayan çalışma alanının tekbaşına bir bütün olarak homojen bir bölge olmadığını ortaya çıkarmıştır (H1=13,60>>1,0).Bu havzaların fizyografik ve hidrolojik özellikleri göz önüne alınarak ve H1kriterine göre, çalışma alanı 11 farklı homojen bölgeye ayrılmıştır. ZDISTuygunluk testi sonuçlarına göre 11 alt bölgenin beşinde Genelleştirilmiş Normaldağılım en uygun olasılık dağılımı olarak belirlenmiştir ve GenelleştirilmişEkstrem Değerler, Pearson Tip 3 ve Genelleştirilmiş Lojistik dağılımlarısırasıyla üç, iki ve bir alt bölgede en uygun dağılım olarak bulunmuştur. Budağılımlarla yapılan indeks‒taşkın frekans analizleri sonucunda, çalışma alanıiçin 2 yıl ile 1000 yıl arası tekerrür periyotlarında grid yöntem ile bölgeselYıllık Anlık Maksimum Akım haritaları oluşturulmuştur., In this study, Regional Frequency Analysis ofannual flood peaks (AFP) series of natural streams discharging to the Black Seausing AFP series recorded 52 stream-gauging stations is done by L-Momentsmethod. Application of method performed by using computer program provided byHosking reveals that working area including five major basins cannot be treatedas a single homogeneous region as a whole (H1=13.6>>1.0).Taking into consideration physiographical and hydrological peculiarities ofthese basins and according to H1 criteria, working area is dividedinto 11 different homogeneous regions. According to ZDISTgoodness-of-fit criterion, Generalized Normal Distribution is determined to bethe best probability distribution in five of eleven sub-regions and GeneralizedExtreme Values, Pearson Type 3, Generalized Logistic distributions are found tobe the best in three, two, and one sub-regions, respectively. As a result of index-floodfrequency analysis with these distributions, regional annual flood peaks mapscovering range of return periods from 2 to 1000 years are produced by gridmethod for working area.

The ability of three different heuristic regression methods, least-square support vector regression (LSSVR), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree), was investigated in forecasting hydrological time series. In the study, daily streamflow data from two stations in Turkey were used by employing cross validation method. Models were compared with each other with respect to root-mean-square error, mean absolute error and determination coefficient. The LSSVR was found to be superior to the MARS and M5Tree models in daily streamflow forecasting. The effect of periodicity component and log transformation was also examined in forecasting daily streamflows by adding day number of each streamflow data and log transformated data as inputs to the applied models. It was observed that the accuracy of LSSVR model was slightly increased by adding periodicity component in both stations while the accuracy of M5Tree model was decreased in forecasting streamflows of Derecikviran station. The accuracy of all the three models was not increased by using log transformation of the daily streamflow data as inputs.

In this study, flood hazard maps were prepared for the Mert River Basin, Samsun, Turkey, by using GIS and Hydrologic Engineering Centers River Analysis System (HEC-RAS). In this river basin, human life losses and a significant amount of property damages were experienced in 2012 flood. The preparation of flood risk maps employed in the study includes the following steps: (1) digitization of topographical data and preparation of digital elevation model using ArcGIS, (2) simulation of flood lows of different return periods using a hydraulic model (HEC-RAS), and (3) preparation of flood risk maps by integrating the results of (1) and (2).