Estimation of CO flux components over northern hemisphere forest ecosystems by using random forest method through temporal and spatial data scanning procedures.

Modeling CO flux components is an important task in ecosystem analysis and terrestrial studies. Net ecosystem exchange (NEE), ecosystem respiration (R), and gross primary production (GPP) are three CO flux components. Despite the ecosystem land cover characteristics, climatic factors can make considerable impact on quantity and mechanism of these components.

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem.

Modeling groundwater quality by using hybrid intelligent and geostatistical methods.

Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems.