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Dynamics of suspended sediment load with respect to summer discharge and temperatures in Shaune Garang glacierized catchment, Western Himalaya

Kumar R., Kumar R., Singh S., Singh A., Bhardwaj A., Kumari A., Randhawa S. S., Saha A.

Acta Geophysica

2018

Vol. 66, no. 5

1109--1120

The observed and predicted rise in temperature will have deleterious impact on melting of snow and ice and form of precipitation which is already evident in Indian Himalayan Region. The temperature-dependent entities like discharge and sediment load will also vary with the observed and predicted rise posing environmental, social and economic threat in the region. There is little known about sediment load transport in relation to temperature and discharge in glacierized catchments in Himalaya mainly due to the scarcity of ground-based observation. The present study is an attempt to understand the suspended sediment load and transportation in relation to variation in discharge and temperature in the Shaune Garang catchment. The result shows strong dependence of sediment concentration primarily on discharge (R2 = 0.84) and then on temperature (R2 = 0.79). The catchments with similar geological and climate setting were observed to have comparatively close weathering rate. The sediment load was found to be higher in the catchments in eastern and central part of Indian Himalayan Region in comparison with western part due to dominance of Indian Summer Monsoon leading to high discharge. The annual physical weathering rate in Shaune Garang catchment was found to be 411 t km−2 year−1 which has increased from 327 t km−2 year−1 in around three decades due to rise in temperature causing increase in discharge and proportion of debris-covered glacierized area.

Developing nonlinear models for sediment load estimation in an irrigation canal

Ahmed F., Hassan M., Hashmi H. N.

Acta Geophysica

2018

Vol. 66, no. 6

1485--1494

The study was performed to estimate the weekly sediment load in Thal canal located in Mianwali district Punjab, Pakistan. Past records of sediments and discharge have been considered as the input parameters. The best input combinations have been identified with the help of advanced algorithms including full, sequential and increasing embedding, genetic algorithm and hill climbing in combination with the gamma test. Model training has been carried out using two artificial neural networkbased algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), back-propagation and a local linear regression technique. A variety of statistical parameters including R square, root mean squared error, mean square error and mean bias error (MBE) has been calculated in order to evaluate the best models. The results strongly suggested that BFGS-based model performed better than all other models with remarkably low values of MBE. Significantly high values of correlation coefficient (R square) in both training and testing evidenced a close similarity between actual and predicted sediment load values for the same model.

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

Tachi S. E., Ouerdachi L., Remaoun M., Derdous O., Boutaghane H.

Journal of Water and Land Development

2016

no. 29

75--81

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.

Ocena i przewidywanie ładunku zawiesiny w rzekach są istotne w zarządzaniu zasobami wodnymi w różnych hydrosystemach. Trudno jest uzyskać efektywne i szybkie oszacowanie ładunku zawiesiny za pomocą sztucznych sieci neuronowych bez uniknięcia przepełnienia danymi. W niniejszej pracy porównano wyniki zastosowania wielowarstwowej sieci w dwóch wariantach – sieci nieregularyzowanej i sieci regularyzowanej z użyciem techniki Early Stopping do oceny i prognozowanie ładunku zawiesiny w rzece Isser powyżej zbiornika Beni Amran w północnej Algierii. Badania bazowały na notowaniach dobowego odpływu zawiesiny i danych dotyczących odpływu wody w ciągu 30 lat (1971–2001). Wyniki modeli opartych na metodzie wstecznej propagacji oceniono za pomocą współczynnika determinacji (R2) i pierwiastka ze średniego błędu kwadratowego. Porównanie wyników dowodzi, że sieć neuronowa regularyzowana przy pomocy techniki Early Stopping celem uniknięcia przeładowania sprawdza się lepiej niż sieć nieregularyzowana. Wyniki wskazują, że przeładowanie wstecznej propagacji ma miejsce z powodu złożoności danych wprowadzonych do sieci.