Seawater intrusion (SWI) is one of the most challenging and widespread environmental problems that threaten the quality and sustainability of fresh groundwater resources in coastal aquifers. The excessive pumping of groundwater, associated with the lack of natural recharge, has exacerbated the SWI problem in arid and semi-arid regions. Therefore, appropriate management strategies should be implemented in coastal aquifers to control the impacts of SWI problems, considering acceptable limits of economic and environmental costs. The management of coastal aquifers involves the identification of an acceptable ultimate landward extent of the saline water body and the calculation of the amount of seaward discharge of freshwater that is necessary to keep the saline–freshwater interface in a seacoast position. This paper presents a comprehensive review of available hydraulic and physical management strategies that can be used to reduce and control SWI in coastal aquifers. Advantages and disadvantages of the different approaches are presented and discussed.

The Gulf Cooperation Council (GCC) countries include Bahrain, Kuwait, Saudi Arabia, Sultanate of Oman, Qatar, and United Arab Emirates. The GCC countries are located in an arid region. They have limited renewable water resources due to scarcity of rainfall. This paper provides the most recent and accurate quantitative and qualitative assessment of available water resources and demands in the GCC countries. The annual renewable surface water, desalinated capacity, wastewater treatment capacity, and per capita water consumption in the GCC countries are assessed. The possible impacts of climate change are discussed. The annual renewable surface water, desalinated capacity, and wastewater treatment capacity in the GCC countries are estimated as 4.14, 26.4, and 10.07 billion m³, respectively. The average per capita water consumption is around 550 l/d. The GCC countries have high water footprints. Although tertiary treated, the reuse of treated wastewater is limited and constrained to the development of forests and green areas. Water demand trends reveal the need for the implementation of sustainable water management programs. Emerging solutions include imposing a new tariff system, improving irrigation efficiency, controlling agricultural water consumption, developing innovative desalination and treatment technologies, maximizing treated wastewater utilization and rainwater harvesting, eliminating leakage in networks, and considering virtual water concepts in the water budget and planning.

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R² values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.