Rain-fed agriculture is the main source of food in Ghana therefore improving quantitative rainfall estimates is essential for local farmers to predict crop growth using vegetation models. Rainfall dynamics in the tropics is an ongoing topic of research due to their complexity and sub-grid precipitation variability. At the same time, tropical areas such as Ghana are the most affected by a lack of proper rain gauge network coverage. Traditional methods rely on empirical assumptions and statistical theories that require continuous calibration and still struggle to accurately represent local variability. The aim of this paper is to demonstrate the potential of a Deep Learning (DL) approach using bi-spectral information of water vapor imagery (WV) and thermal infrared (TIR) as a starting point to develop an effective alternative to the Cold Cloud Duration (CCD) method which is a widely applied statistical technique by satellite rainfall products like Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Tropical Applications of Meteorology using SATellite data (TAMSAT) that are specifically designed for Africa. WV inhibition of low-level features assures the correct depiction of strong convective motions usually related to heavy rainfall which is crucial in tropical areas where convective rainfall is dominant. The addition of WV 7.3𝜇m is particularly beneficial in North Ghana as tropical systems are advecting dry air from the nearby Sahara desert creating discontinuities in precipitation events which translates into dry intrusions and dry slots seen in the images of the WV channel. The developed Deep learning model showed strong performances in binary classification where it outperformed IMERG-Final false alarms count resulting in lower rainfall overestimation (FBias < 2.0), although further research is needed to overcome the very poor relation between GEO-IR images and actual rainfall estimates at the surface.

On request of Royal Boskalis Westminster N.V. a comprehensive research was performed regarding emission free maintenance dredging in a harbour environment. The project site is the Maasmond at the port of Rotterdam. It covers an area of almost 10 km2 and, on average, a monthly volume of 400.000 cubic meters of sediment needs to be dredged. The operations are currently performed using trailing suction hopper dredgers (TSHD). Several new fully working emission free concept work methods were designed. These were assessed using a multi-criteria analysis, where emphasis was placed on energy reduction, reliability, interference, risk and safety. Given the scope of this research, costs are not decisive. General conclusions for the solutions contain the splitting of the total process. As the energy consumption of a conventional hopper is too high to operate on a battery cell, the work method is split into three different processes being: (i) gathering, (ii) pumping and (iii) transportation. Two work methods scored best in this research, Sloped Water Injection Dredging (SWID) and the Fully Autonomous Submerged Dredger (FASD). SWID consists of Water Injection Dredging vessels, fixed structures and autonomous barges. FASD contains the design of a submerged dredging vessel. It can be concluded that a harbour environment is suitable to perform emissions free maintenance dredging with only small alterations to the current technology.