Karst aquifers, capable of storing and transmitting large amount of water, are the main source of drinking water in many regions worldwide. Their excessive permeability leads to an enhanced vulnerability to retain and spread the contamination accordingly. From sustainability perspective, the environmental, economic and social impacts of karst contamination on water resources management are gaining more attention. In this study, an overview of hydrogeological processes and concepts regarding groundwater flow and contaminant transport in karstic systems is presented, followed by a short discussion on surface water and groundwater interaction. Due to the complexity of karstic systems, different approaches have been developed by researchers for investigating and understanding hydrogeological processes and groundwater behavior in karst which are reviewed herein. Additionally, groundwater contamination issues and the most common and effective remediation techniques in karstic terrains are discussed. Lastly, modeling techniques and remote sensing methods, as beneficial and powerful tools for assessing groundwater flow and contaminant transport in karst terrains, are reviewed and evaluated. In each section, relevant research works conducted for Puerto Rico are discussed and some recommendations are presented to complement the ongoing hydrogeological investigations on this island.

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Long term measurements of the amount of solar energy at ground level are not easily possible in many locations. Therefore, using empirical relations and recently applying Artificial Neural Networks (ANN) are common means for prediction of the available solar energy at desired areas. Recent studies indicate that the performance of ANN provides better prediction than empirical relations. In former researches about ANN modeling of solar energy for some geographical locations, the parameters such as maximum and minimum daily temperature, relative humidity and wind speed were considered as the input of the soft computing. In present Multilayer Perceptron (MLP) ANN modeling, the amount of suspended Particulate Matters (PM 10 and PM 2.5 ) in the atmosphere is also added to the soft computation input. This ANN modeling strategy is used for estimating the amount of daily absorption of global solar radiation (both beam and diffuse radiation) on the land surface of Tehran (Longitude 51.23N and Latitude 35.44E) during a year. Furthermore, Indexes of Root Mean Square Error (RMSE), Absolute Fraction of Variance (R 2 ) and Mean Absolute Percentage Error (MAPE) are used for accuracy evaluation of modeling results.

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