Towards a modelling, optimization and predictive control framework for smart irrigation.

Smart irrigation scheduling is a promising approach for improving the efficiency and sustainability of agricultural water use, especially in arid and semi-arid lands. In this paper, we present a design and simulation of a model predictive controller for smart irrigation scheduling. The proposed controller is based on a mathematical model of the irrigation system, which is used to predict the future states of the system and determine the optimal irrigation schedule for a given crop and field.

Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region.

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts.

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana.

The main objective of this study was to map the quality of groundwater for domestic use in the Nabogo Basin, a sub-catchment of the White Volta Basin in Ghana, by applying machine learning techniques. The study was conducted by applying the Random Forest (RF) machine learning algorithm to predict groundwater quality, by utilizing factors that influence groundwater occurrence and quality such as Elevation, Topographical Wetness Index (TWI), Slope length (LS), Lithology, Soil type, Normalize Different Vegetation Index (NDVI), Rainfall, Aspect, Slope, Plan Curvature (PLC), Profile Curvature (PRC), Lineament density, Distance to faults, and Drainage density. The groundwater quality of the area was predicted by building a Random Forest model based on computed Arithmetic Water Quality Indices (WQI) (as dependent variable) of existing boreholes, to serve as an indicator of the groundwater quality.

Nitrate contamination of groundwater in the Lower Volta River Basin of Ghana: Sources and related human health risks.

A significant population within the Lower Volta River Basin of Ghana relies solely on untreated groundwater (GW) and surface water (SW) for various purposes. However, negative practices associated with increasing human activities pose threats to particularly GW quality in the basin. Using NO as a proxy, this study mainly focused on the status of GW contamination, origins of NO and potential human health risks through integrated hydrochemistry, correlation analysis, isotopes (N, δO), Bayesian and USEPA human health risk models.

Analysis of climate and anthropogenic impacts on runoff in the Lower Pra River Basin of Ghana.

The Lower Pra River Basin (LPRB), located in the forest zone of southern Ghana has experienced changes due to variability in precipitation and diverse anthropogenic activities. Therefore, to maintain the functions of the ecosystem for water resources management, planning and sustainable development, it is important to differentiate the impacts of precipitation variability and anthropogenic activities on stream flow changes. We investigated the variability in runoff and quantified the contributions of precipitation and anthropogenic activities on runoff at the LPRB.