An evaluation of the aquifer properties of the inland sedimentary Bida basin, central Nigeria, was conducted using empirical methods derived from particle size distribution curves. The main aquifer properties determined were hydraulic conductivity, porosity, effective porosity and coefficient of uniformity. Samples for analysis were obtained from test water wells drilled to 100m in selected parts of the Basin. The empirical method used was the Hazens method, while porosity was determined in the laboratory. The results show that three levels of aquifers generally exist in the Basin. The aquifer material consist of well sorted medium sand to fine gravel. The upper aquifer occurs at a depth of between 10 -18m and has a hydraulic conductivity of 18.5m/d and effective porosity of 9.0. The second, or middle, aquifer occurs at a depth of between 45 -65m and has a hydraulic conductivity of 37m/d and effective porosity of 20. The third, or lower, aquifer occurs between 80 -100m and has a hydraulic conductivity of 32m/d and effective porosity of 24. Effective porosity generally increases with depth in the basin indicating coarsening up of the sandstone with fewer fine grained cementing material. Mean hydraulic conductivity value is 29.16m/d, porosity 63%, effective porosity of 6.7 and coefficient of uniformity of 2.8. The results have therefore shown that it is possible to obtain quantitative results from particle size distribution curves that are useful for the determination of hydraulic properties of aquifers.

Riverine flood is a major disaster faced by most countries and has significant adverse effect on long term economic growth of affected regions and their environments. Several systems have previously employed different technologies to monitor riverine flood but are expensive with low accuracy and consumes high amount of energy. In this paper, we proposed an energy efficient and accurate flood monitoring system. The system leverages on Internet Protocol Version 6 over Low Power Wireless Personal Area Network (6loWPAN) technology to construct a Wireless Sensor Network (WSN) comprising of two XM1000 motes and a rule-base water level monitoring application. The motes were configured using NesC programming for flood monitoring with Basestation and water level sensing applications. The water level sensing mote samples and transmits real-time water level information to the Basestation mote which interfaces with a rule-based water level monitoring application. The application compares current water level with a predetermined threat level and alerts relevant agencies when flood is imminent via an email. The results obtained from the emulation of the developed system showed that, it achieved an accuracy of 95.3% in water level monitoring with a Mean Squared Error of 5.1. The power consumed in transmitting a packet of 2 bytes payload plus other overhead was 0.4µJ and 0.0396mJ with and without 6loWPAN configuration respectively.

BACKGROUND Access to electricity supply is fundamental to the achievement of any meaning development in a nation. However, over two billion people across the world lack access to electricity (International Energy Agency (IEA), 2018). More than 620 million people in Sub-Saharan Africa (SSA) are believed to be without power (IEA, 2014) and about 60% of Nigerians do not have access to electricity despite the abundant energy resources available in the country (IEA, 2019). Going by the current electricity deficit and population growth projections, the number of people worldwide without access to electricity may surpasses 2.5 billion people by 2030 if current electricity supply and distribution is not improved (IEA, 2017). In Nigeria, there is the rural urban dichotomy on electricity access. According to the International Energy Agency (2016), electricity access in Nigeria is at 55 percent in urban areas and 36 percent in rural areas, while about 134 million people (76 percent) rely on traditional biomass for energy. Majority of the population without access to electricity are located in the country's rural areas, far away from existing and usually poor grid network. Extending electricity to these communities (rural communities) come with huge investments, structural, and technological changes in energy system (Barnes, 2005; Alexandros et al., 2018). Therefore, if these challenges must be overcome, a more flexible and sustainable electrification scheme must be developed in a cost-effective manner (Mentis, 2016). Hydropower is among the most efficient technologies for production of renewable electrical energy, with