Creating an efficient model for predicting sea level fluctuations is essential for climate change research. This study examined the effectiveness of utilizing Artificial Neural Networks (ANNs), particularly the recurrent network approach. ANNs were chosen for their capacity to learn from extensive and intricate data and their ability to handle nonlinear correlations. The Long Short-Term Memory (LSTM) algorithm was employed to fill data gaps and predict future sea level records in the Arabian Gulf, especially in Mina Salman. The results were promising, with LSTM successfully filling a 6-year data gap while maintaining low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The first phase of the model yielded a RMSE value of 63.4 mm and a MAPE value of 3.14%. The same approach was used to retrain the model with a mix of real and predicted values, preserving historical patterns and yearly rates with an RMSE of 66.5 mm and a MAPE of 3.07%. These findings highlight LSTM’s advantages when considering only historical information for predicting the future sea level changes. The research provides valuable insights into predicting sea level changes in regions with limited field data, such as the Arabian Gulf, and emphasizes the potential for further research to enhance sea level prediction models through improved optimization techniques.

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination ($R^2$), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an $R^2$ greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models’ precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models’ effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions.

The COVID-19 pandemic has led to significant shifts in global water consumption, particularly affecting wastewater treatment plants (WWTPs). In Kuwait, where high residential water usage exists, the lockdowns resulting from the pandemic created a unique opportunity to evaluate the effects of altered human activity on wastewater characteristics. This study examines the effects of the lockdown on key wastewater parameters, including flow rate, COD, BOD, TSS, total Kjeldahl nitrogen (TKN), and total phosphorus (TP). Data were collected from four WWTPs in Kuwait over 4 to 8 years, with the Kabd WWTP providing continuous daily data for an 8-year period. A comparative analysis was conducted between pre-lockdown, lockdown, and post-lockdown periods using statistical methods such as paired t-tests. The study also integrates a global comparison to relate Kuwait’s findings. Results indicate a significant increase in wastewater flow (7.6%) during the lockdown, rising from 165,486 m³/d to 178,033 m³/d. COD and BOD levels increased by 27.1% and 18.9%, respectively, while TSS showed the largest rise at 29.9%. TKN increased by 20.1%, indicating higher nitrogenous waste contributions from residential sources. These findings highlight the pandemic’s impact on wastewater characteristics in Kuwait, driven primarily by increased domestic water consumption. The study underscores the necessity of adaptive wastewater management strategies, especially in semi-arid regions, where WWTPs must be equipped to handle unexpected changes in wastewater composition. This research provides essential insights for improving the flexibility of wastewater systems with future disruptions, contributing to both environmental management and public health awareness.