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Lakes are seriously affected due to urban pollution. The study of the morphological features of a lake system helps to identify its environmental status. The objective of the present study is to analyse the influence of morphometry on water quality in a lake (Akkulam-Veli Lake, Thiruvananthapuram, Kerala). The morphological features namely mean depth, surface area, volume, shoreline length, shoreline development and index of basin permanence have been evaluated. Correlation analysis has been conducted to determine the relationship between morphological features and water quality. Regression analysis has been conducted to find out the extent of influence of morphometric features on water quality. The study revealed that the lake is less affected by wind-induced wave action due to various reasons. The depth and volume have significant role in the water quality. The nitrogen fixation of blue green algae can be observed from the morphological features. The morphology has greater role in the water quality of a lake system.

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The remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam-Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6-LISS III) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud-free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three-layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.