## 3 Results
### Current Status, Challenges and Future Developments
#### 3.2.2 Renewable Hydrogen Storage
Evaluation criteria include maps of roads, gas pipelines, water, and borehole locations, as well as maps like storage capacity, land use, and energy consumption; while exclusion criteria showed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
protected areas around the rock salt deposits, including national forests, special protection areas, protected areas, conservation areas, and ecological sites. Results showed a good performance of the CNN compared with another advanced method such as AHP. In this line, [PERSON], [PERSON] et al. [100] applied a two-stage decision framework for optimal site selection, based on GIS as the first step, and on the Large-scale Group Decision-making method (LSGDM) as the second step. The weights of the criteria are established based on experts' opinions with the probabilistic linguistic terms. In this way, optimal sites are determined in China for wind-photovoltaic-hybrid energy storage projects including hydrogen energy storage and electric thermal energy storage. In parallel, the use of IoT and geophysical methods facilitates the detection and characterization of natural H\({}_{2}\), particularly in low concentrations, while overcoming challenges related to subsurface uncertainty and non-invasive assessment. [PERSON], [PERSON] et al. [107] have developed a methodology to detect natural hydrogen seepage from remote sensing data, provided that their visual imprint can be easily confused with that of salt lakes. With this aim, a combination of IoT measurements (salinity, soil gas sampling) with geophysics (X-Ray diffraction) has been applied, finding that surface geology has a more recent mark in the case of hydrogen than for salt lakes. Additionally, it has been concluded that the determination of the origin of hydrogen would require the monitoring of surface features for a longer period of weeks to better understand the evolution of the gas mix seeping above a structure. In [PERSON], [PERSON] et al. [106] the focus was on multi-gas monitoring, with which the authors detected that hydrogen was commonly accompanied by CO\({}_{2}\) and Rn. This led to the understanding that the generation of hydrogen took place due to mantle rocks serpentinization, and that it traveled to the surface along major thrusting faults that are well-imaged with geophysical methods. The detection of hydrogen leakage is also key for the management of the storage and in order to ensure its safety. For this reason, [PERSON], [PERSON] et al. [119] have combined ML with a geophysical technique such as seismic data to detect and characterize hydrogen leakage. Their advance lies in the possibility of detecting hydrogen from sparse data, avoiding the requirement of continuous measurements. On the contrary, [PERSON] et al. [113] have developed a methodology based on Raman spectroscopy for the real-time and minute-analysis of hydrogen leakage. Their system is validated in simulation tests, especially focusing on the avoidance of interference due to the detection of other gases such as CO. [PERSON], [PERSON] et al. [114] have also developed a real-time monitoring system based on geophysics, consisting of a network of piezoelectric sensors. Their system focuses on hydrogen vessels instead of underground storage, in such a way that the implementation of laboratory and real-life tests was possible. In addition to hydrogen detection, the integration of simulations and artificial intelligence further enhances the modeling of underground storage behavior and design, offering predictive capabilities essential for optimizing system configuration and storage performance. An example of this is the work by [PERSON], [PERSON] et al. [95], consisting of the development of a spatio-temporal Mix-Integer Linear Programming (MILP) model coupled with GIS modeling that can simultaneously optimize the design, planning and operation of integrated energy value chains, identifying candidate sites for energy production and optimizing scenarios for the production of renewable hydrogen.