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Evaluation of the effects of land-use change and increasing deforestation in the Sapanca Basin on total suspended solids (TSS) movement with predictive models

Temiz Temel, Sonmez Osman, Dogan Emrah, Oner Adnan, Opan Mücahit

Acta Geophysica

2022

Vol. 70, no 3

1331--1347

Sapanca Lake is a tectonically sourced freshwater resource and one of the rare natural water resources used as a source of drinking water. This study examined the change of land use and lake area in the natural water source basin subjected to human pressure for years. Landsat 5 TM (1987) and Landsat 8 TM (2010) satellite images were used. Satellite images were analyzed using ArcGIS 10.1 software. As a result of the analysis, it was observed that the natural vegetation was significantly destroyed between 1987 and 2010. Besides, the bathymetry maps of Lake Sapanca belonging to the years 1990 and 2010 were also examined, and accordingly, it was determined that there was a 2% reduction in the lake surface area. The decrease in the volume of the lake was thought to be due to sedimentation movement caused by land-use change, and the total amount of suspended solids, grain size, discharge, and temperature measurements were made between 2012 and 2014 in 12 streams which are sources of Sapanca Lake. Sediment prediction models have been developed under two different scenarios using measurement data from side streams. Artificial neural networks (ANN), Sediment rating curve, and multiple linear regression models were examined within the scenario models, and comparisons were made between the models. It was determined that ANN achieved the closest results with the measurement data.

Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria

Beddal Dalila, Achite Mohammed, Baahmed Djelloul

Journal of Water and Land Development

2020

no. 47

16--24

Streamflow modelling is a very important process in the management and planning of water resources. However, complex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hydrometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.