Vaibhav Garg

Reservoir Sedimentation Estimation Using Artificial Neural Network

Conventional methods and models available for estimation of reservoir sedimentation process differ greatly in terms of complexity, inputs, and other requirements. An artificial neural network (ANN) model was used to estimate the volume of sediment retained in a reservoir. Annual rainfall, annual inflow, and capacity of the reservoir were chosen as inputs. Thirty Two years of data pertaining to Gobindsagar Reservoir on the Satluj River in India, were used in this study (23 years for training and 9 years for testing). The pattern of the sediment volume retained in this reservoir was well captured by the Multi-Layer Perceptron (3–5-1) ANN model using the back propagation algorithm. Based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional regression analysis.

Re-look to conventional techniques for trapping efficiency estimation of a reservoir

Abstract All reservoirs are subjected to sediment inflow and deposition up to a certain extent leading to reduction in their capacity. Thus, the important practical problem related to the life of reservoir is the estimation of sedimentation quantity in the reservoirs. Large number of methods and models are available for estimation of reservoir sedimentation process. However, each model differs greatly in terms of their complexity, inputs and other requirements. In the simplest way, the fraction of sediment deposit in the reservoir can be determined through the knowledge of its trap efficiency. Trap efficiency ( T e ) is the proportion of the incoming sediment that is deposited or trapped in a reservoir. Most of the T e estimation methods define a relationship of the T e of the reservoir to their capacity and annual inflow, generally through curves. In this study, the empirical relationships given by Brune and Brown were used and compared for estimating the trap efficiency of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district of Himachal Pradesh, in the Himalayan region of India. A new set of regression equations has been developed for Brunes method and compared with Brown and other available Brunes equations. It has been found that Brunes equations developed in the present study estimated better than the other Brunes equations reported in literature. Later, in the present study it was found that Browns approach was over estimating the T e . Hence it was again modified for Gobindsagar reservoir. It was also identified that sediments coming to this particular reservoir were mainly of coarse nature.

Evaluation of reservoir sedimentation using data driven techniques

Abstract The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets.

Sediment Yield Assessment of a Large Basin using PSIAC Approach in GIS Environment

Reservoirs are the key infrastructure for the socio-economic development of a country. The reservoirs are proven to be a remedial solution of highly erratic spatial and temporal availability of water. The growth in population and consequent developmental activities within a catchment area has shown to aggravate the problem of sedimentation which comprised of erosion, sediment transport and its deposition in these reservoirs. Among all above mentioned, reservoir sediment deposition is most important as it reduces its useful life and impairs the purposes of these vast water resource. The sediment yield has been considered as comprehensive index for assessing sustainability of such resources. The present study investigates the suitability of Pacific Southwest Inter-Agency Committee (PSIAC) model in determining the sediment yield rate for a drainage basin considering nine basin factors in geographical information system (GIS) environment. For the analysis, a large river basin at the foothill of Himalayas in India has been considered as case study. It was realized that the GIS approach made large basin characteristic sampling very easy and efficient for this hilly basin. A regression equation between specific sediment yield and effective model factors was established based on geomorphic features for this basin. It was observed that most of the basin area is falling under moderate to high sediment yielding potential zone, leading to high sediment yield.

Modeling the Time Variation of Reservoir Trap Efficiency

All reservoirs are subjected to sediment inflow and deposition to a certain extent resulting in reduction of their capacity. Trap efficiency ( Te ) , a most important parameter for reservoir sedimentation studies, is being estimated using conventional empirical methods till today. A limited research has been carried out on estimating the variation of Te with time. In the present study, an attempt has been made to incorporate the age of the reservoir to estimate the Te . This study investigates the suitability of conventional empirical approaches along with soft computing data-driven techniques to estimate the reservoir Te . The incorporation of reservoir age, in empirical model, has resulted in a better Te estimation. Further, to estimate Te at different time steps, soft computing approaches such as artificial neural networks (ANNs) and genetic programming (GP) have been attempted. Based on correlation analysis, it was found that ANN model (4–4-1) resulted better than conventional empirical methods but in...

Assessment of the effect of slope on runoff potential of a watershed using NRCS-CN method

The rainfall-runoff is a very complex hydrological phenomenon, as this process is highly non-linear, time-varying and spatially distributed. The average slope within the watershed together with the overall length and retardance of overland flow are considered to be the main factors which govern the runoff process. The natural resources conservation service curve number (NRCS-CN), formerly known as soil conservation services curve number, is the most widely used method to estimate direct runoff from rainfall, due to its simplicity and the use of the single CN parameter. However, the NRCS-CN method has been developed for limited watershed area and slope. In the present study, the modified NRCS-CN method for slope and CN conversion have been investigated to determine runoff potential of a watershed in geo-spatial environment. Solani watershed, which is a sub-watershed of Ganga basin located partly in Uttarakhand and Uttar Pradesh states of India; has been considered for analysis. The daily rainfall-runoff study has been carried out for year 2006. It was found that slope factor effects runoff estimation significantly.

TRAP EFFICIENCY ESTIMATION OF A LARGE RESERVOIR

ABSTRACT Sediment deposited or trapped in a reservoir can easily be quantified by the simple knowledge of its trap efficiency (T e ). In the present study methods proposed by Brown and Brune have been adopted to estimate T of Pong Reservoir (Beas Dam) on the Beas River in Kangra district of Himachal Pradesh, India. The necessary modifications in the adopted methods have been made using the available data for this reservoir. This modification is basically to take into account the variation in trap efficiency with time.

Inductive Group Method of Data Handling Neural Network Approach to Model Basin Sediment Yield

AbstractMost of the hydrological models developed and used previously in sediment yield modeling are complex and lack general applicability. Moreover, the availability of sediment data for the development and calibration of such models is very scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent years, multidisciplinary artificial intelligence techniques—namely, artificial neural networks (ANNs)—have shown the capability to solve such complex nonlinear systems. This study investigates the suitability of an inductive group method of data handling polynomial neural network (GMDH-NN) technique in estimating sediment yield. The data on various meteorological and geomorphological features—namely, river length, watershed area, erodible area, average slope of watershed, annual average rainfall, and drainage density—from 20 subwatersheds of the Arno River Basin in Italy were used for model development. T...

Modeling catchment sediment yield: a genetic programming approach

Hydrologic processes are complex, non-linear, and distributed within a watershed both spatially and temporally. One such complex pervasive process is soil erosion. This problem is usually approached directly by considering the sediment yield. Most of the hydrologic models developed and used earlier in sediment yield modeling were lumped and had no provision for including spatial and temporal variability of the terrain and climate attributes. This study investigates the suitability of a recent evolutionary technique, genetic programming (GP), in estimating sediment yield considering various meteorological and geographic features of a basin. The Arno River basin in Italy, which is prone to frequent floods, has been chosen as case study to demonstrate the GP approach. The results of the present study show that GP can efficiently capture the trend of sediment yield, even with a small set of data. The major advantage of the GP analysis is that it generates simple parsimonious expression offering some possible interpretations to the underlying process.

Hypothetical scenario–based impact assessment of climate change on runoff potential of a basin

Climate change and its impact on hydrological processes are causes for widespread concern and challenge. In this regard, large numbers of global and regional circulation models (GCMs and RCMs) have been developed to study the future climate. However, these GCMs and RCMs have large uncertainties in defining climate scenarios. The uncertainties associated with these models provided an opportunity to investigate the impact of climate change on the runoff potential of a major basin in India under different assumed plausible hypothetical scenarios. These scenarios were developed by increasing the temperature by 1°C, 2°C and 3°C and rainfall by 5%, 10% and 15%, and then combinations of both. Understanding the hydrologic response of very large river basins again poses a huge challenge to hydrologists. Therefore, an attempt has been made to exploit the capabilities of a variable-infiltration-capacity macro-scale hydrological model to simulate the Satluj river basin. It was found that a slight change in climate may cause huge differences in the hydrological regime of the basin.