Evaluating the long-term variability of the seawater salinity and temperature due to climate change is a limiting economical and operational factor in planning the design of new and expansion of existing desalination plants. This need is amplified in the Arabian Gulf due to the natural arid climate and anthropological stresses related to energy exploration and ongoing major developments. The lack of data in this region further adds additional dimension to the problem. The present work represents a systematic innovative approach to evaluate the anticipated long-term changes in the seawater salinity and temperature under the stresses of projected climate change and massive industrial effluents using statistical correlation and hydrodynamic simulation. The proposed approach employs the direct relation between the net freshwater losses (evaporation) entrenched with the investigated stressors and the mean sea salinity and sea temperature variation of an inverse estuary to formulate the statistical correlation and the hydrodynamic simulation conditions.

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott’s index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.