WATER-Model: An Optimal Allocation of Water Resources in Turkey, Syria and Iraq

Political instability of several countries in the Middle East is overshadowing one of the biggest challenges of the upcoming century: Water - a natural resource that is easily taken for granted, but whose scarcity might lead to serious conflicts. This paper investigates an optimal Water Allocation of the Tigris and Euphrates Rivershed by introducing the WATER-Model. A series of scenarios are analyzed to examine the effects of different levels of cooperation for an optimal water allocation. Special emphasize is put on the effects of filling new Turkish reservoirs which can cause additional welfare losses if these actions are not done on a basin-wide coordinated basis. Modeling results show that Turkey is most efficient in its water usage. However, using the water for irrigation purposes in Turkey, instead of the Iraqi or Syrian domestic and industrial sector, decreases the overall welfare. Especially the Euphrates basin might thus encounter losses of up to 33% due to such strategic behaviour. The predicted water demand growth in the region is going to increase this water scarcity further. Minimum flow treaties between riparian countries, however, can help to increase the overall welfare and should therefore be fostered

Seismic evidence of tectonic stresses; Implications for basin reconstruction

Stress and strain are two important rheological parameters that have impacts on basin development and dynamics. The dynamic evolution of a basin depends on the spatial and temporal changes in the stresses. How to determine the reference state of stress within a sedimentary basin and the magnitude of the forces at the plate boundaries, which produce an important part of these stress fields, have been two of the most important questions in basin research for the past few decades. The stress history within a basin is also one of the key parameters in the formation of petroleum provinces. In addition, it has ecological impacts, because erosion and other surface processes are affected by stress induced basin topography. None of the conventional methods of mapping regional stress fields provides a reasonable spatial continuity. The conventional methods use earthquake focal mechanisms, well bore breakouts, drilling induced fractures, in-situ stress measurements and young geological data from fault slip analysis as stress indicators. Although these indicators lead to construction of successful world stress maps, the information is mostly restricted to one-dimension. Numerical geomechanical modelling techniques improve the quantification of stress in multi-dimensional space, but these techniques can reach only a limited accuracy due to unavailability of input elastic material parameters in multi-dimensional space.Civil Engineering and Geoscience

Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection

Cognitive radars are systems that rely on learning through interactions of the radar with the surrounding environment. To realize this, radar transmit parameters can be adapted such that they facilitate some downstream task. This paper proposes the use of deep reinforcement learning (RL) to learn policies for gain control under the object detection task. The YOLOv3 single-shot object detector is used for the downstream task and will be concurrently used alongside the RL agent. Furthermore, a synthetic dataset is introduced which models the radar environment with use of the Grand Theft Auto V game engine. This approach allows for simulation of vast amounts of data with flexible assignment of the radar parameters to aid in the active learning process.Comment: 5 pages, 5 figures, conferenc

Linking dynamic elastic parameters to static state of stress: toward an integrated approach to subsurface stress analysis.

Stress is the most important parameter to understand basin dynamics and the evolution of hydrocarbon systems. The state of stress can be quantified by numerical geo-mechanical modelling techniques. These techniques require static elastic parameters of the rocks as input, while tectonic and gravitational forces are given as explicit boundary conditions to compute the local state of stress at different scales. We developed a technique to determine the density and elastic constants at seismic frequencies using full Zoeppritz inversion on angle-dependent seismic reflection data. The dynamic elastic parameters as obtained from seismic data differ from their static equivalents, which are necessary to determine the static state of stress. The dynamic elastic parameters are related to their static equivalents through experimentally obtained relations. In these rock-physics experiments, the static and dynamic elastic parameters are measured simultaneously during different external loading conditions. The experiments used here are all carried out in a tri-axial pressure machine under equal axial stresses. Then pre-stack seismic data analysis in combination with the relation between the static and dynamic elastic parameters, from the rock-physics experiments, provides the input parameters for geo-mechanical modelling. © 2004 Elsevier B.V. All rights reserved

Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection

Cognitive radars are systems that rely on learning through interactions of the radar with the surrounding environment. To realize this, radar transmit parameters can be adapted such that they facilitate some downstream task. This paper proposes the use of deep reinforcement learning (RL) to learn policies for gain control under the object detection task. The YOLOv3 single-shot object detector is used for the downstream task and will be concurrently used alongside the RL agent. Furthermore, a synthetic dataset is introduced which models the radar environment with use of the Grand Theft Auto V game engine. This approach allows for simulation of vast amounts of data with flexible assignment of the radar parameters to aid in the active learning process.</p

Turkey

This chapter reviews irrigation development and policy with specific references to the main water- and land-based regional socioeconomic development projects in Turkey. It analyzes the expansion of irrigation investment as well as institutional and technological changes in irrigation policy and development in parallel with policies of liberalization and decentralization in the late 1980s. The chapter also discusses institutional changes in the management of the irrigation systems as a result of (partial) transfer of management of large-scale irrigation systems to a variety of water user organizations. Finally, it describes current technological and institutional problems and the further challenges to the irrigation sector, such as infrastructure deterioration, risks of drought, environmental and ecological system degradation, and insufficient investment. It also notes the efforts to equip new irrigation schemes with modern technology, such as closed pipes for conveying water instead of open channels, and water-saving micro-irrigation methods rather than surface irrigation techniques.WOS:000486992000009Scopus - Affiliation ID: 60105072Book Citation Index- Science - Book Citation Index- Social Sciences and HumanitiesArticle; Book ChapterNisan2019YÖK - 2018-1