@article{LorekWagner,
  author    = {Lorek, Andreas and Wagner, Norman},
  title     = {Supercooled interfacial water in fine grained soils probed by dielectric spectroscopy},
  series = {Cryosphere},
  journal   = {Cryosphere},
  doi       = {10.5194/tc-7-1839-2013},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170516-31840},
  pages     = {1839 -- 1855},
  abstract  = {Water substantially affects nearly all physical, chemical and biological processes on the Earth. Recent Mars observations as well as laboratory investigations suggest that water is a key factor of current physical and chemical processes on the Martian surface, e.g. rheological phenomena. Therefore it is of particular interest to get information about the liquid-like state of water on Martian analogue soils for temperatures below 0 °C. To this end, a parallel plate capacitor has been developed to obtain isothermal dielectric spectra of fine-grained soils in the frequency range from 10 Hz to 1.1 MHz at Martian-like temperatures down to -70 °C. Two Martian analogue soils have been investigated: a Ca-bentonite (specific surface of 237 m2 g-1, up to 9.4\% w / w gravimetric water content) and JSC Mars 1, a volcanic ash (specific surface of 146 m2 g-1, up to 7.4\% w / w). Three soil-specific relaxation processes are observed in the investigated frequency-temperature range: two weak high-frequency processes (bound or hydrated water as well as ice) and a strong low-frequency process due to counter-ion relaxation and the Maxwell-Wagner effect. To characterize the dielectric relaxation behaviour, a generalized fractional dielectric relaxation model was applied assuming three active relaxation processes with relaxation time of the ith process modelled with an Eyring equation. The real part of effective complex soil permittivity at 350 kHz was used to determine ice and liquid-like water content by means of the Birchak or CRIM equation. There are evidence that bentonite down to -70 °C has a liquid-like water content of 1.17 monolayers and JSC Mars 1 a liquid-like water content of 1.96 monolayers.},
  subject      = {Grundwasser},
  language  = {en}
}
@article{BandJanizadehChandraPaletal.,
  author    = {Band, Shahab S. and Janizadeh, Saeid and Chandra Pal, Subodh and Chowdhuri, Indrajit and Siabi, Zhaleh and Norouzi, Akbar and Melesse, Assefa M. and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration},
  series = {Sensors},
  volume    = {2020},
  journal   = {Sensors},
  number    = {Volume 20, issue 20, article 5763},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/s20205763},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43364},
  pages     = {1 -- 23},
  abstract  = {Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70\%) and testing (30\%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.},
  subject      = {Grundwasser},
  language  = {en}
}