scholarly journals ESTIMATION OF EROSION AND SEDIMENTATION USING SEDIMENTOLOGICAL DATA AND CALCULATION OF SUSPENDED SEDIMENT LOAD IN GARMSAR WATERSHED

Geosaberes ◽  
2020 ◽  
Vol 11 ◽  
pp. 334
Author(s):  
Gohar Mirakhorlo ◽  
Mohammad Nakhaei ◽  
Mohammadreza Espahbod ◽  
Davoud Jahani ◽  
Khalil Rezaei
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Least Squares Method ◽  
Initial Study ◽  
Suitable Model ◽  
Suspended Sediment Load ◽  
Statistical Regression ◽  
Temporal Separation ◽  
Discharge Data ◽  
Flow Discharge

Erosion, sediment transport, sedimentation and water quality are very important issues in watershed management. Based on the initial study, it was found that the three factors of geological materials, slope, and climate are the most important factors in erosion. Therefore, these three factors were examined and combined to create work units. Then, the qualitative sensitivity of the rocks and pre-Quaternary formations was continually determined using the criteria of resistance method and hardness of the rock mass in a field method. Then, several statistical regression models were investigated by discharge classification and temporal separation of data; and by establishing a regression relation between flow discharge and sediment discharge data and its simulation, sedimentation rating curves along with FAO and USBR were presented based on the error least squares method according to statistical analysis methods, and annual long run suspended sediment load values were estimated by combining the proposed models and flow discharge methods such as flow continuity curve, daily mean discharge, and monthly mean discharge. In this regard, the statistics of a 13-year period of Bankooh hydrometric station on the HablehRood River were used. Finally, combination of the classes mean model with the mean daily discharge by FAO method was introduced as a suitable model.

scholarly journals A new approach for suspended sediment load calculation based on generated flow discharge considering climate change

2021 ◽  
Author(s):  
Arash Adib ◽  
Ozgur Kisi ◽  
Shekoofeh Khoramgah ◽  
Hamid Reza Gafouri ◽  
Ali Liaghat ◽  
...  
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Sediment Load ◽  
Time Series Data ◽  
Series Data ◽  
Suspended Sediment Load ◽  
Discharge Time ◽  
Discharge Data ◽  
New Approach ◽  
Flow Discharge

Abstract Use of general circulation models (GCMs) is common for forecasting of hydrometric and meteorological parameters, but the uncertainty of these models is high. This study developed a new approach for calculation of suspended sediment load (SSL) using historical flow discharge data and SSL data of the Idanak hydrometric station on the Marun River (in the southwest of Iran) from 1968 to 2014. This approach derived sediment rating relation by observed data and determined trend of flow discharge time series data by Mann-Kendall nonparametric trend (MK) test and Theil-Sen approach (TSA). Then, the SSL was calculated for a future period based on forecasted flow discharge data by TSA. Also, one hundred annual and monthly flow discharge time series data (for the duration of 40 years) were generated by the Markov chain and the Monte Carlo (MC) methods and it calculated 90% of total prediction uncertainty bounds for flow discharge time series data by Latin Hypercube Sampling (LHS) on Monte Carlo (MC). It is observed that flow discharge and SSL will increase in summer and will reduce in spring. Also, the annual amount of SSL will reduce from 2,811.15 ton/day to 1,341.25 and 962.05 ton/day in the near and far future, respectively.

scholarly journals Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Siyamak Doroudi ◽  
Ahmad Sharafati ◽  
Seyed Hossein Mohajeri
Keyword(s):  
Support Vector Regression ◽  
Suspended Sediment ◽  
Water Resource Management ◽  
Sediment Load ◽  
Pearson Correlation ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
Unknown Parameters ◽  
Support Vector Regression Model ◽  
Discharge Data

Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. This study considers the support vector regression (SVR) method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization (OTLBO) method is integrated with the SVR model to provide a novel hybrid predictive model. The SVR combined with the genetic algorithm (SVR-GA) is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of rainfall and discharge data of Cham Siah River catchment are provided. The predictive models are assessed using various numerical and visual indicators. The results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBO model offers highest Pearson correlation coefficient (R = 0.9768), Willmott’s Index (WI = 0.9812), ratio of performance to IQ (RPIQ = 0.9201), and modified index of agreement (md = 0.7411) and the lowest relative root mean square error (RRMSE = 0.5371) in comparison with SVR-GA (R = 0.9704, WI = 0.9794, RPIQ = 0.8521, and md = 0.7323, 0.5617) and SVR (R = 0.9501, WI = 0.9734, RPIQ = 0.3229, md = 0.4338, and RRMSE = 1.0829) models, respectively.

scholarly journals Temporal Variation Analyses of Suspended Sediment Load in an Agricultural Catchment and its Land Use Implications

2018 ◽  
Vol 1 ◽  
pp. 1
Author(s):  
Masayaki Fujihara ◽  
Edward Lapong ◽  
Tomoki Izumi ◽  
Noriyuki Kobayashi
Keyword(s):  
Land Use ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Least Squares Method ◽  
Seasonal Pattern ◽  
Temporal Distribution ◽  
Nonlinear Least Squares ◽  
Suspended Sediment Load ◽  
Rating Curves ◽  
Nonlinear Least Squares Method

The suspended sediment load of a small agricultural river was estimated using suspended sediment rating curves established using discharge-suspended sediment discharge correlation and stratified aggregate or seasonally clustered data; and the results were correlated to the land use of the watershed. The results showed that: (a) on regression, nonlinear least squares method in establishing rating curves produced significantly better and more efficient suspended sediment rating curves; (b) seasonally clustering the data produced better suspended sediment rating curves; (c) based on statistical and physical relations, suspended sediment load in the catchment followed a clear cyclical seasonal pattern; and (d) the land use and agricultural activities, other than rainfall, had a significant impact on the temporal distribution and variability of the suspended sediment load. Read full article here.

scholarly journals Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

2021 ◽  
Vol 14 (18) ◽  
Author(s):  
Maryam Asadi ◽  
Ali Fathzadeh ◽  
Ruth Kerry ◽  
Zohre Ebrahimi-Khusfi ◽  
Ruhollah Taghizadeh-Mehrjardi
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Morphometric Parameters ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
K Nearest Neighbor ◽  
Discharge Data ◽  
Vector Machines

AbstractEstimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. It is desirable to obtain accurate estimates of sediment load while using techniques that limit computational intensity when datasets are large. This study investigates the usefulness of geo-morphometric factors and machine learning (ML) models for predicting suspended sediment load (SSL) in several river basins in Lorestan and Gilan, Iran. Six ML models, namely, multiple linear regression (MLR), artificial neural networks (ANN), K-nearest neighbor (KNN), Gaussian processes (GP), support vector machines (SVM), and evolutionary support vector machines (ESVM), were evaluated for estimating minimum and average SSL for the study regions. Geo-morphometric parameters and river discharge data were utilized as the main predictors in modeling process. In addition, an attribute reduction technique was applied to decrease the algorithm complexity and computational resources used. The results showed that all models estimated both target variables well. However, the optimal models for predicting average sediment load and minimum sediment load were the GP and ESVM models, respectively.

scholarly journals Forecasting suspended sediment load using regularized neural network: Case study of the Isser River (Algeria)

2016 ◽  
Vol 29 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Salah E. Tachi ◽  
Lahbassi Ouerdachi ◽  
Mohamed Remaoun ◽  
Oussama Derdous ◽  
Hamouda Boutaghane
Keyword(s):  
Neural Network ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Water Discharge ◽  
Back Propagation ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Suspended Sediment Load ◽  
Early Stopping ◽  
Discharge Data

Abstract In the management of water resources in different hydro-systems it is important to evaluate and predict the sediment load in rivers. It is difficult to obtain an effective and fast estimation of sediment load by artificial neural network without avoiding over-fitting of the training data. The present paper comprises the comparison of a multi-layer perception network once with non-regularized network and the other with regularized network using the Early Stopping technique to estimate and forecast suspended sediment load in the Isser River, upstream of Beni Amran reservoir, northern Algeria. The study was carried out on daily sediment discharge and water discharge data of 30 years (1971–2001). The results of the Back Propagation based models were evaluated in terms of the coefficient of determination (R2) and the root mean square error (RMSE). Results of the comparison indicate that the regularizing ANN using the Early Stopping technique to avoid over-fitting performs better than non-regularized networks, and show that the overtraining in the back propagation occurs because of the complexity of the data introduced to the network.

scholarly journals Land-Use/-Cover Changes and Their Effect on Soil Erosion and River Suspended Sediment Load in Different Landscape Zones of European Russia during 1970–2017

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1631
Author(s):  
Artyom V. Gusarov
Keyword(s):  
Soviet Union ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Gully Erosion ◽  
European Russia ◽  
Relative Reduction ◽  
Forest Steppe ◽  
Suspended Sediment Load ◽  
Cultivated Land ◽  
Study Region

Contemporary trends in cultivated land and their influence on soil/gully erosion and river suspended sediment load were analyzed by various landscape zones within the most populated and agriculturally developed part of European Russia, covering 2,222,390 km2. Based on official statistics from the Russian Federation and the former Soviet Union, this study showed that after the collapse of the Soviet Union in 1991, there was a steady downward trend in cultivated land throughout the study region. From 1970–1987 to 2005–2017, the region lost about 39% of its croplands. Moreover, the most significant relative reduction in cultivated land was noted in the forest zone (south taiga, mixed and broadleaf forests) and the dry steppes and the semi-desert of the Caspian Lowland—about 53% and 65%, respectively. These territories are with climatically risky agriculture and less fertile soils. There was also a widespread reduction in agricultural machinery on croplands and livestock on pastures of the region. A decrease in soil/gully erosion rates over the past decades was also revealed based on state hydrological monitoring data on river suspended sediment load as one of the indicators of the temporal variability of erosion intensity in river basins and the published results of some field research in various parts of the studied landscape zones. The most significant reduction in the intensity of erosion and the load of river suspended sediment was found in European Russia’s forest-steppe zone. This was presumably due to a favorable combination of the above changes in land cover/use and climate change.

scholarly journals Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

2021 ◽  
Author(s):  
Hamid Darabi ◽  
Sedigheh Mohamadi ◽  
Zahra Karimidastenaei ◽  
Ozgur Kisi ◽  
Mohammad Ehteram ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Soft Computing ◽  
Sediment Load ◽  
Optimization Algorithms ◽  
Training Phase ◽  
Suspended Sediment Load ◽  
Engineering Structures ◽  
Northern Iran ◽  
Taylor Diagram ◽  
Computing Models

AbstractAccurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.

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