According to the ICOLD (International Commission on Large Dams), the majority of failed dams either did not have any monitoring system or had a system that was out of order [1]. This finding therefore demonstrates the importance of inspection and an appropriate monitoring system for regular observation of dam performance. The objective of dam monitoring, which plays a significant role in the concept of dam safety, is to provide data in order to evaluate dam performances throughout its whole life cycle. The typical safety control variables could be classified into 3 categories: mechanical effects (deformation, displacement), hydraulic effects (seepage flow rate and pore water pressure) and environmental effects (reservoir water levels, precipitation and temperature) [2]. Such variables are quantified by means of monitoring instruments installed in dams. Once the monitoring data is collected, it is necessary to analyze it inside and in the vicinity of the dam for the purpose of determining and understanding the dam’s behaviour. Changes in the behaviour of dams in response to thermal or seasonal effects and to variations in the reservoir water level are mostly reversible. By separating the hydrostatic effect induced by the impounding variations and the thermal effects induced by the temperature variation, some aspects can be better understood. It should be noted that the thermal effects are negligible for embankment dams, and thus can be neglected for analyzing the measurements collected in such dams. Multiple correlation methods are used to draw up models for the following-up and surveillance of monitoring measurements. Over the last fifty years, the increase of knowledge in the field of data analysis has led to the development of analytical methods which can exploit these databases, yielding excellent results [3]. The first physical-statistical model which statistically determines the effects of hydrostatic and thermal loads was formulated in 1967 [4,5]. This HST model accounts for mechanical behaviour, by using a statistical regression technique to find out correlations between causes and quantified effects. It is based on a mean seasonal thermal reference curve for the phenomena observed on dams and it enables one-year periodic variations to be identified according to the reservoir level and time. After many years using this model, its limitation was revealed by the phenomena which are sensitive to variations of temperature because it cannot take real temperature into account. In 2004, a new model named HST-T (-T for thermal) [6] was developed after the heatwave of 2003 to better consider the influences of harsh thermal conditions. Concerning embankment dams, the monitoring of pore water pressure is crucial because it is the main indicator of internal erosion and seepage problems and has a significant role in the stability of geotechnical works [7–۹]. Pagano [10] focused on these pore water pressure measurements at different stages of the dam lifetime. The authors collected measurements, particularly during the consolidation process within the dam, in order to detect the effectiveness of measurements, in revealing watertightness problems. These problems are influenced by various external factors due to the fact that the effects of variations in reservoir water levels are not instantaneous since the flow inside the dams are delayed by the low hydraulic conductivity of the materials constituting the embankment.