Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

In the future, renewable energy technologies will have a significant role in catering to energy security concerns and a safe environment. Among the various renewable energy sources available, biomass has high accessibility and is considered a carbon-neutral source. Pyrolysis technology is a thermo-chemical route for converting biomass to many useful products (biochar, bio-oil, and combustible pyrolysis gases). The composition and relative product yield depend on the pyrolysis technology adopted. The present review paper evaluates various types of biomass pyrolysis. Fast pyrolysis, slow pyrolysis, and advanced pyrolysis techniques concerning different pyrolyzer reactors have been reviewed from the literature and are presented to broaden the scope of its selection and application for future studies and research. Slow pyrolysis can deliver superior ecological welfare because it provides additional bio-char yield using auger and rotary kiln reactors. Fast pyrolysis can produce bio-oil, primarily via bubbling and circulating fluidized bed reactors. Advanced pyrolysis processes have good potential to provide high prosperity for specific applications. The success of pyrolysis depends strongly on the selection of a specific reactor as a pyrolyzer based on the desired product and feedstock specifications.

Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.