The selection of an appropriate spillway has a significant effect to the construction of a dam and several procedures and considerations are needed. In the past, this selection of the type of the spillway was arbitrary and sometimes with bad results. Recently the Multiple Criteria Decision Making theory has given the possibility to make a decision about the optimum form of a spillway under complex circumstances. In this paper, the above method is used and especially the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for the selection of a spillway for a dam in the district of Kilkis in Northern Greece—‘Dam Pigi’. As the criteria were fuzzy and uncertain, the Fuzzy TOPSIS method is introduced together with the AHP (Analytic Hierarchy Process), which is used for the evaluation of criteria and weights. Five types of spillways were selected as alternatives and nine criteria. The criteria are expressed as triangular fuzzy numbers in order to formulate the problem. Finally, using the Fuzzy TOPSIS method, the alternatives were ranked and the optimum type of spillway was obtained.

Rainfall erosivity concerns the ability of rainfall to cause erosion on the surface of the earth. The difficulty in modeling the distribution, the size, and the terminal velocity of raindrops in relation to the detachment of soil particles led to the use of more tractable rainfall indices. Thus, in the universal soil loss equation (USLE), the coefficient of rainfall erosivity, R, was introduced. This coefficient is based on the product of the rainfall kinetic energy of a storm and its maximum 30-minute intensity. An important problem in the application of USLE and its revisions in various parts of the world concerns the computation of R, which requires pluviograph records with a length of at least 20 years. For this reason, empirical equations have been developed that are based on coarser rainfall data, such as daily, monthly, or yearly, which are available on larger spatial and temporal extents. However, the lack of denser data is dealt more effectively by means of machine learning methods. Computational systems for this purpose were recently developed based on feed-forward neural networks, yielding significantly better results.

Hydraulic Conductivity (K) is an important hydraulic parameter as it affects the environment by controlling infiltration, irrigation rate, and consequently the water movement through the ground. In order to determine Hydraulic Conductivity in a soil column during unsaturated flow, experiments were performed in the laboratory. A sandy (S) soil sample of known Hydraulic Conductivity at saturation (KS) was placed uniformly in a transparent column. Using a pump, water was applied at the surface of the soil column in certain supplies (Qi), while soil moisture (θ) was measured using TDR probes. At the same time, soil pore pressure (h) was measured using pressure transducers. The cumulative volume of the outgoing water (V) of the column was measured. Experimental data were fitted by Van Genuhten's Hydraulic Conductivity model. The results of the above experimental procedure constitute useful tools for the simulation of water movement in unsaturated soils and can be the outset for further research