scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

scholarly journals Monthly Suspended Sediment Load Estimation using Artificial Neural Network and Traditional Models in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 613-618
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Tectonic Activity ◽  
Precipitation Seasonality ◽  
Artificial Neural

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

scholarly journals Estimation of Suspended Sediment Yield using Artificial Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 3249-3253
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Yield ◽  
Godavari River ◽  
Artificial Neural

Estimation of the suspended sediment yield is important for the planning and management of water resources and protection of the environment. Environmental change influences sediment generation and the transport and the consequent sediment load in river. In this study, artificial intelligence-based technique like the artificial neural network (ANN) is proposed for sediment yield estimation in the Godavari river basin, India. The ANN is one of the appropriate data-mining techniques that help model the complex phenomenon of sedimentation. In this study the prediction of the suspended sediment load is done using the ANN techniques by using the water discharge and water level data from 1970 to 2015 as inputs at Polavaram gauge station in Godavari river basin, India. The results demonstrate that the ANN shows a satisfactory performance based on the root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE) and correlation coefficient (r) error statistics and provided more accurate results.

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Ahmad Shakir Mohd Saudi ◽  
Hafizan Juahir ◽  
Azman Azid ◽  
Mohd Khairul Amri Kamarudin ◽  
Mohd Fadhil Kasim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
River Basin ◽  
Stream Flow ◽  
Pearson Correlation ◽  
Suspended Solid ◽  
Test Analysis ◽  
Risk Class ◽  
Artificial Neural

Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

scholarly journals Contemporary Trends in River Flow, Suspended Sediment Load, and Soil/Gully Erosion in the South of the Boreal Forest Zone of European Russia: The Vyatka River Basin

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2567
Author(s):  
Artyom V. Gusarov ◽  
Aidar G. Sharifullin ◽  
Achim A. Beylich
Keyword(s):  
Land Use ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Load ◽  
Water Discharge ◽  
East European Plain ◽  
Suspended Sediment Load ◽  
Forest Zone ◽  
East European ◽  
Erosion Intensity

Recent decades in the north of the East European Plain have been characterized by significant changes in climate and land use/cover, especially after the collapse of the USSR in 1991. At the same time, the hydrological consequences of these changes, especially changes in erosion processes and river sediment load, have been studied insufficiently. This paper partially covers this existing knowledge gap using the example of the Vyatka River basin. Draining an area of 129,000 km2, the Vyatka River is among the largest rivers in the boreal forest zone of European Russia. Cultivated land occupies about one-fifth of the river basin area; about three-fourths is covered by taiga forest vegetation. The results of state long-term hydrometeorological monitoring and information on land use/cover made it possible to reveal contemporary (since the 1960s) hydrological and erosion-intensity trends and their drivers within the greater (96%) part of the river basin. There has been a statistically insignificant increase in water discharge in the Vyatka River basin during recent decades. This is due to a statistically insignificant increase (for the entire basin studied) in the spring snowmelt-induced floodwater flow and a statistically significant rise in the discharge in the year’s warm and cold seasons. The main reason for the detected trends is increased precipitation, including heavy rainfall during the warm season. In contrast to this, the total annual suspended sediment load of the river (especially that which was snowmelt-induced) and, consequently, soil/gully erosion intensity have experienced a significant decrease in recent decades (up to 58% between 1960–1980 and 2010–2018). Land-use/-cover changes (a reduction of cultivated land area and agricultural machinery, a decline of livestock in pastures) following the collapse of the Soviet Union are considered the main reasons for this decrease. The most noticeable changes in water discharge, suspended sediment load, and erosion intensity were observed in the most agriculturally developed southwest and south parts of the Vyatka River basin. All the above trends may be considered with a high probability to be representative for the south sector of the taiga zone of the East European Plain.

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