Leakage detection and localization in District Heating Networks (DHNs) remains critical to maintain operational reliability and minimize economic and energy losses. Three different data-driven and model-based approaches have been proposed to solve this problem and delivered promising results: an approach for detecting and evaluating leakage-induced pressure waves (PWD), a numerical-analytical approach based on a district heating network model (MBSE) and a purely data-driven approach (ML). All these approaches rely on current measurement data from the network, i.e. pressure, flow rate and temperature. These approaches have been continuously improved ever since. The MBSE approach, which was previously based on a purely hydraulic DHN model, has been extended to include the thermal model equations, which allows better consideration of the available temperature measurement data. This temperature measurement data is also used by the ML approach in order to estimate the resulting potential for improvement with better preselections. These approaches are applied to the same historical measurement data of real DHN leakage events used in a previous study to evaluate the performance enhancements. First, the approaches are evaluated independently to quantify their individual improvements. Subsequently, as previously demonstrated, their interoperability is examined to exploit potential synergies to narrow down their search space and effectively locate leakages. The results are compared to the baseline established in the aforementioned study, highlighting the impact of the methodological extensions on the overall leakage localization performance. By combining the refined individual results of the three approaches, this study not only emphasizes the respective strengths of each method, but also underlines the importance of combining their capabilities to achieve a robust and efficient leakage localization framework for DHNs.