Various sectors of the economy such as transport and renewable energy have shown great interest in sea bed models. The required measurements are usually carried out by ship-based echo sounding, but this method is quite expensive. A relatively new alternative is data obtained by airborne lidar bathymetry. This study investigates the accuracy of these data, which was obtained in the context of the project ‘Investigation on the use of airborne laser bathymetry in hydrographic surveying’. A comparison to multi-beam echo sounding data shows only small differences in the depths values of the data sets. The IHO requirements of the total horizontal and vertical uncertainty for laser data are met. The second goal of this paper is to compare three spatial interpolation methods, namely Inverse Distance Weighting (IDW), Delaunay Triangulation (TIN), and supervised Artificial Neural Networks (ANN), for the generation of sea bed models. The focus of our investigation is on the amount of required sampling points. This is analyzed by manually reducing the data sets. We found that the three techniques have a similar performance almost independently of the amount of sampling data in our test area. However, ANN are more stable when using a very small subset of points.

Developments in agriculture, industry, and urban life have caused the deterioration of water resources, such as rivers and reservoirs in terms of their quality and quantity. This includes the Saguling Reservoir located in the Citarum Basin, Indonesia. A review of previous studies reveals that the water quality index ( WQI) is efficient for the identification of pollution sources, as well as for the understanding of temporal and spatial variations in reservoir water quality. The NSFWQI (The National Sanitation Foundation water quality index) is one of WQI calculation methods. The NSFWQI is commonly used as an indi-cator of surface water quality. It is based on nitrate, phosphate, turbidity, temperature, faecal coliform, pH, DO, TDS, and BOD. The average NSFWQI has been 48.42 during a dry year, 43.97 during a normal year, and 45.82 during a wet year. The WQI helped to classify water quality in the Saguling Reservoir as “bad”. This study reveals that the strongest and most significant correlation between the parameter concentration and the WQI is the turbidity concentration, for which the coeffi-cient correlation is 0.821 in a dry year, and faecal coli, for which the coefficient correlation is 0.729 in a dry year. Both parameters can be used to calculate the WQI. The research also included a nitrate concentration distribution analysis around the Saguling Reservoir using the Inverse Distance Weighted method.