The routes of warplanes are planned confidentially, and they are not shared with any organization in advance. In some cases, border violations may occur, and as a result, it increases the tension between two states. This situation puts many people at risk and impairs the prestige of the state both economically and socially. In this paper, Ontology-Based Instantaneous Route Suggestion System (SUARSIS) based on semantic approach is proposed to predict and plan routes of warplanes before they reach their target. In the proposed system, we developed an architecture called Ontology-based Route Suggestion by using the OWL (Web Ontology Language) language with realistic data. The aircraft model, aircraft fuel system, features of the military field, and the relations in the semantic context are logically defined through ontology. Synthetic scenarios were created to validate the accuracy of the proposed method. Experimental results show that the proposed system has a good performance on predicting warplane routes.

Streamflow forecasting holds a vital role in planning, design, and management of basin water resources. Accurate streamflow forecast provides a more efficient design of water resources systems technically and economically. In this study, various machine learning algorithms were evaluated to model monthly streamflow data in the Coruh river basin, Turkey. The dataset contains the mean monthly streamflow between 1963 and 2011. For the machine learning model, Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbours (KNN) and Random Forest algorithms were considered and compared. Based on the test scores of the considered models with the hyperparameters, Random Forest based model outperforms all other models.