A Case Study for Determination of the Best Underground Dam Sites, Bursa Province, Turkey.

Water constitutes an indispensable resource vital for sustaining life. In this context, groundwater stands out as a paramount global water source. Throughout history, underground dams (UGDs) have been employed to augment the storage capacity of local aquifers. This study employs a multistep elimination approach to identify optimal locations for constructing UGDs in the Bursa district, Turkey. Initially, the Digital Elevation Model (DEM) is utilized to pinpoint the potential construction sites at the watershed scale. Criteria such as suitable topographic slope range, proximity to the transport infrastructures, presence of natural or artificial reservoirs, distance to active or inactive faults, proximity to the urban and rural settlements, location of the irrigation zones, geological conditions, distance to the consumption hubs, thickness of alluvium layer, and the groundwater depth are used to establish the buffer zones for exclusion of potential sites. Then, storage volume in the proposed sites is determined, and formal requests from the local communities are taken into consideration for determining the best UGD sites. The study concludes that five UGDs for irrigation and one for drinking water purposes could be recommended for further implementation.

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL.

Disposal of solid waste in Istanbul and along the Black Sea coast of Turkey.

The increasing amount of solid waste arising from municipalities and other sources and its consequent disposal has been one of the major environmental problems in Turkey. Istanbul is a metropolitan city with a current population of around 14 million, and produces about 9000 ton of solid waste every day. The waste composition for Istanbul has changed markedly from 1981 to 1996 with large decreases in waste density, much of which is related to decreased amounts of ash collected in winter. In recent years, the Istanbul region has implemented a new solid waste management system with transfer stations, sanitary landfills, and methane recovery, which has led to major improvements. In the Black Sea region of Turkey, most of the municipal and industrial solid wastes, mixed with hospital and hazardous wastes, are dumped on the nearest lowlands and river valleys or into the sea. The impact of riverside and seashore dumping of solid wastes adds significantly to problems arising from sewage and industry on the Black Sea coast. Appropriate integrated solid waste management systems are needed here as well; however, they have been more difficult to implement than in Istanbul because of more difficult topography, weaker administrative structures, and the lower incomes of the inhabitants.