Department of Agricultural and Food Engineering

1. Estimation of Monthly Silt Load using Bootstrap-Based Artificial Neural Networks (BANN’s) for Upper Damodar Valley Catchments (DVC) Department of Agricultural and Food Engineering Indian Institute of Technology, Kharagpur-721302, India by Sanjay Kumar Sharma Research Scholar Guided by Prof. K. N. Tiwari

2. Introduction • The dawn of this century has witnessed the emergence of hydrological issues as one of the major concerns posed in front of modern civilization. • With increasing pressures on land, the natural balance has been affected leading to serious problems of soil erosion and land degradation. • Estimation, conservation and management of available water play a vital role in sustaining the increasing requirements. • The rainfall- runoff-silt load is a complex hydrological phenomenon to simulate due to the spatial-temporal variability and inter-relationships of underlying climatic, physiographic, and topographic variables.

3. DVC • Damodar Valley Catchments (DVC) constitute one of the prime River Valley Projects (RVP) in the country. • The upper catchments of the Damodar River system is infested with serious problems of land degradation by soil erosion. • It is estimated that about 6.6 Lakh hectares in the catchment area requires immediate attention for erosion control. • The silt gets deposited into the four big reservoirs namely Tilaiya, Konar, Maithon and Panchet constructed by DVC reducing the life of the Multi Purpose Reservoirs.

4. DVC UDVC LDVC UDVC Area =17513.08 km2

7. Objectives To explore the inclusion of soil, topographic, geomorphologic and vegetation (STGV) attributes for estimation of monthly silt load. To minimize the number and inter-correlations of STGV attributes using Principal Component Analysis (PCA). To determine the significant combinations of STGV attributes by hierarchical bootstrap based ANN (BANN) modeling for prediction of monthly silt load. To generate annual map of silt load for entire UDVC using the best performing ANN.

8. Neural Networks • A network consists of many elements or ‘neurons’ that are connected by communication channels or connectors’ carrying numeric data organized into layers. • The neural networks can perform a particular function when certain values are assigned to the connections or ‘weights’ between elements.

9. Mathematical Representation • The mathematical model of a neural network comprises of a set of simple functions linked together by weights. • The network consists of a set of input units x, output units y, and hidden units z, which link the inputs to outputs. • The hidden units extract useful information from inputs and use them to predict the outputs.

10. Neural Networks • The training of a network is accomplished using an optimization • procedure (such as nonlinear least squares). • The objective is to minimize the sum of squares of the residuals • between the measured and predicted output. • Where; • Ns is the number of samples, No is the number of outputs, • W and U are the weights of the hidden and output layer.

11. Bootstrap Method • Error decreases as the number of Measurements increase. • Accuracy of the neural-networks prediction can be enhanced by • generating multiple models and aggregating them to produce an • estimate • Bootstrap aggregating Breiman, 1996) manipulates the training • data in order to generate different models and using these to get • an aggregated predictor • Bootstrap method (Efron and Tibshirani, 1993) assesses the • accuracy of a prediction by sampling with replacement the • training data.

12. Bootstrap Method • Bootstrap assumes that the training data set is a representation • of the population, and multiple realizations of the population can • be simulated from a single dataset. • This is done by repeated ‘sampling with replacement’ of the • original dataset of size N to obtain B bootstrap data sets, each • of the size N. • Suppose a training data is Z = {(x1, y1), (x2, y2), , (xn, yn)}. • We draw B datasets each of size N of the training data by • sampling with replacement. For each of the bootstrap dataset • Zb, b = 1, 2, …, B, we fit model (f )b x . • The bootstrapping estimate is calculated as the mean of each • model:

13. Data Collection

14. Methodology TOPOGRAPHY SOILS WATERSHED GEOMORPHOLOGIC PARAMETERS VEGETATION RAINFALL Shape Indices Network Indices Elevation Primary Terrain Attributes Secondary Terrain Attributes Compound Terrain Attributes Soil Physical Properties NDVI Rainfall (Monthly) • Calculation of Basic Statistics • Correlation Matrix • Principal Component Analysis

15. SOILS TOPOGRAPHY VEGETATION WATERSHED GEOMORPHOLOGIC PARAMETERS Basic Statistics for choosing : - Relevant input parameters - Relevant input combinations RAINFALL MODELS M1 = R + S M2 = R + S + T M3 = R+ S + T + V TRAINING : 2/3rd DATASETS VALIDATION : 1/3rd DATASETS BOOTSTRAPPING NEURAL NETWORKS PREDICTION: All the unmonitored microwatersheds of Upper DVC Silt Load

17. Details of Hydrologic Data Collected from watersheds

21. Derived Input Parameters

22. Principal Component Analysis

23. Performance comparison of best neural network models for given inputs for prediction of monthly silt load

24. Annual Silt Load Map Generation of Map

27. Conclusions • The mapping of annual silt load for UDVC demonstrates the usefulness of incorporating topography and vegetation parameters along with watershed geomorphologic and soil inputs • This study recommends the coupling of statistical techniques (PCA), soft computing tools (BANN) and the use of remote sensing and GIS platforms for better simulations of rainfall-silt load relationships.

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