title: ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.
abstract: The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been considered as the main controlling factors of variations in sediment concentration in the dynamic glacial environment of Gangotri. Fourteen feed forward neural networks with error back propagation algorithm have been created, trained and tested for prediction of sediment concentration. Seven models (T1-T7) have been trained and tested in the non-updating mode whereas remaining seven models (T1a-T7a) have been trained in the updating mode. The non-updating mode refers to the scenario where antecedent time (previous time step) values are not used as input to the model. In case of the updating mode, antecedent time values are used as network inputs. The inputs applied in the models are either the variables mentioned above as individual factors (single input networks) or a combination of them (multi-input networks). The suitability of employing antecedent time-step values as network inputs has hence been checked by comparative analysis of model performance in the two modes. The simple feed forward network has been improvised with a series parallel non-linear autoregressive with exogenous input (NARX) architecture wherein true values of sediment concentration have been fed as input during training. In the glacial scenario of Gangotri, maximum sediment movement takes place during the melt period (May-October). Hence, daily data of discharge, rainfall, temperature and sediment concentration for five consecutive melt periods (May-October, 2000-2004) have been used for modelling. High Coefficient of determination values [0.77-0.88] have been obtained between observed and ANN-predicted values of sediment concentration. The study has brought out relationships between variables that are not reflected in normal statistical analysis. A strong rainfall: sediment concentration and temperature: sediment concentration relationship is shown by the models which are not reflected in statistical correlation. It has also been observed that usage of antecedent time-step values as network inputs does not necessarily lead to improvement in model performance.
authors.affiliation: Department of Earth Sciences, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India, nsingh05@gmail.com.
authors.forename: Nandita
authors.initials: N
authors.lastname: Singh
authors.forename: G J
authors.initials: GJ
authors.lastname: Chakrapani
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D019015:Geologic Sediments; D007194:India; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011891:Rain; D045483:Rivers; D013696:Temperature