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Estimation of Sediment Concentration Using Sediment Rating Curve Approach in Isser Watershed (North-West of Algeria)

Baloul Djouhra, Ghenim Abderrahmane Nekkache, Megnounif Abdesselam

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 6

282--291

EN

This paper was elaborated in order to estimate suspended sediment in Isser watershed located in North-West of Algeria. Power functions models by least squares regression were developed to establish rating curves. In total, 2026 pairs of instantaneous water discharge (Q) and instantaneous suspended sediment concentration (C) from the period 1988–1989 to 2003–2004 were used. In order to reveal the temporal impact, four subdivision data are elaborated: all, annual, seasonal and monthly scales. Better estimation of the total suspended sediment yield was obtained by application of the power linear model in monthly division data (-0.16%). When considering efficiency, both models with seasonal scale offered coefficients in order of 0.95%. The approach of correction for models did not improve accuracy of the estimation. The results have indicated for the 16 years of the available data, that the Isser watershed has drained a total of 194.37 million m3 of water and 1.4 million tons of suspended sediments, with a specific degradation of 77 tons/km2/year.

2

Selection of level and type of decomposition in predicting suspended sediment load using wavelet neural network

Reddy Beeram Satya Narayana, Pramada S. K., Roshni Thendiyath

Acta Geophysica

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2022

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Vol. 70, no 2

847--857

EN

The advancements in artificial intelligence play a significant role in solving the problems of researchers and engineers to develop prediction models with higher accuracy over the analytical and numerical models. The wavelet ensemble artificial intelligence model has a widespread application in forecasting hydrological datasets. The signal decomposition type, level and the mother wavelet affect the model performance in wavelet-based approaches. The present analysis focuses on studying the significance of the level and type of decomposition in wavelet transform for pre-processing the input variables to predict the target variable. In this work, to forecast seasonal suspended sediment load of the Kallada River basin in Kerala, two types of decomposition with decomposition levels ranging from 2 to 7 were adopted using seasonal flow data (wet and dry seasons). To rank the WANN models, compromise programming was adopted using the results based on statistical performance indicators and compared with the performance of the conventional FFNN model. From the accuracy assessment and ranking, type-2 with 5th level decomposition can capture the actual periodicity of the signal and predict the suspended sediment load with higher accuracy. It also shows the capability to predict the extreme events of time series.

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Evaluation of the effects of land-use change and increasing deforestation in the Sapanca Basin on total suspended solids (TSS) movement with predictive models

Temiz Temel, Sonmez Osman, Dogan Emrah, Oner Adnan, Opan Mücahit

Acta Geophysica

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2022

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Vol. 70, no 3

1331--1347

EN

Sapanca Lake is a tectonically sourced freshwater resource and one of the rare natural water resources used as a source of drinking water. This study examined the change of land use and lake area in the natural water source basin subjected to human pressure for years. Landsat 5 TM (1987) and Landsat 8 TM (2010) satellite images were used. Satellite images were analyzed using ArcGIS 10.1 software. As a result of the analysis, it was observed that the natural vegetation was significantly destroyed between 1987 and 2010. Besides, the bathymetry maps of Lake Sapanca belonging to the years 1990 and 2010 were also examined, and accordingly, it was determined that there was a 2% reduction in the lake surface area. The decrease in the volume of the lake was thought to be due to sedimentation movement caused by land-use change, and the total amount of suspended solids, grain size, discharge, and temperature measurements were made between 2012 and 2014 in 12 streams which are sources of Sapanca Lake. Sediment prediction models have been developed under two different scenarios using measurement data from side streams. Artificial neural networks (ANN), Sediment rating curve, and multiple linear regression models were examined within the scenario models, and comparisons were made between the models. It was determined that ANN achieved the closest results with the measurement data.

4

Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques

Üneş Fatih, Taşar Bestami, Demirci Mustafa, Zelenakova Martina, Kaya Yunus Ziya, Varçin Hakan

Rocznik Ochrona Środowiska

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2021

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Tom 23

117--137

EN

Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.