Flood Hazard Assessment in Agricultural Areas: The Case of the District of Pélébina in the Municipality of Djougou, Bénin

Flood is a natural disaster and causes loss of life and property destruction. Its impacts are large on agriculture especially in West African countries where smallholder farmers account for 80% of all the farms. The objective of this study was to assess flood risks in the inland valley of Dosir located in the district of Pélébina, northern Benin. Rainfall, discharge and water level in the riverbed were monitored using rain gauge, acoustic current meter and pressure sensors, respectively. The hydrological functioning of the inland valley was simulated using the Integrated Flood Analysis System (IFAS) model. The calibration was done based on the land use map (GlobalMap) and the soil water holding capacity map (UNEP). Our study demonstrated the existence of a high flood hazard in the inland valley of Dosir which reacts very quickly to rainfall solicitations. The IFAS model has shown a good performance in simulating the runoff in the riverbed of the inland valley with a coefficient of determination of 0.65. The IFAS model can be used to design a flood management system in the district of Pelebina. Further studies are needed to assess the exposure and vulnerability of farmers to flood risk

Proton exchange membrane fuel cell operation and degradation in short-circuit.

International audienceThis paper presents an experimental study dealing with operation and degradation during an electrical short circuit of a proton exchange membrane fuel cell stack. The physical quantities in the fuel cell (electrical voltage and current, gas stoichiometry, pressures, temperatures and gas humidity) are studied before, during and after the failure. After a short circuit occurs, a high peak of current appears but decreases to stabilize in a much lower value. The voltage drops in all the cells and even some cells presents reversal potentials. The degradation is quantified by using electrochemical impedance spectroscopy

Physico-Chemical and Microbiological Qualities of Water From Wells, Drillings and Tanks Used as Drinking Water in the Municipality of Allada (Benin, West Africa)

Water, source of life, is also a source of disease when it is polluted. The aim of this study is to analyze the physicochemical quality and the bacteriological quality of the wells, boreholes and tank for drinking water in the Commune of Allada. The methodology is based on the collection of data, data processing and analysis carried out at the Laboratory for Quality Control of Water and Food (LCQEA) of the Ministry of Health. From the water sampling carried out at three (03) traditional wells, two (02) boreholes, two (02) tanks and water of river (02), bacteriological and physicochemical analysis were performed. The results showed that pH is higher at the tank than other water sources. Well 3 (P3) has a very high electrical conductivity (EC) which was 384.95 μS / cm and 192.47 mg / L for total dissolved solids (TDS). The tank 2 exhibited high value in pH 9.14; 71.72 (μS / cm) for the electrical conductivity (CE) and 35.86 mg / L, in total dissolved solids (TDS). Well 2 (P2) has a high turbidity of 4.53 (NTU) at all analyzed water points. The concentration of iron, copper nickel and cobalt remains low(less than 0.4 mg / L).Wells 2 and tank 1 are concentrated in lead, respectively 20.75mg / L and 13.71mg / L. Tank 1 and 2 have a high concentration of cadmium compared to other water points. The presence of Escherichia coli with a high concentration at home SONEB (39 CFU) and at well 2 (7.10 2 ) was found. In view of these results some recommendations were made

Using an intelligent vision system for obstacle detection in winter condition

This paper explores the performance of an Advanced Driving Assistance System (ADAS) during navigation in urban traffic and a winter condition. The selected ADAS technology, Mobileye, has been integrated into a hydrogen electric vehicle. A set of three cameras (visible spectrum) has also been installed to give a surrounding view of the test vehicle. The tests were carried out during the dusk as well as in the night in winter condition. Using Matlab, the messages provided by Mobileye system have been analyzed. More than 2800 samples (short sequences of 5s Mobileye messages) have been processed and compared with the corresponding video samples recorded by the three cameras. In average, the selected ADAS device was able to provide 99% of true positive vehicle detection and classification, even in poor ambient lighting condition in winter. However, 72% of samples involving a pedestrian was correctly classified

CORRESPONDANCES ENTRE SAVOIRS LOCAUX ET SCIENTIFIQUES : PERCEPTIONS DES CHANGEMENTS CLIMATIQUES ET ADAPTATIONS ETUDE EN REGION COTONNIERE DU NORD DU BENIN

N° ISBN - 978-2-7380-1284-5International audienceA survey carried out in four regions of Benin with different climatic profiles have shown producers' perceptions on climatic changes, their consequences and the different adapted measures taken by producers in order to deal with such consequences. Methodologies used comprise group discussions as well as individual interviews with open-ended, semi open and open questionnaires. The present paper is based on research results from 110 family farms of two villages in the northern region. Climate is Sudanese and the production system is cotton based. Recent climate trends perceived by producers are: irregular rainfalls, shortening of cultivation season, occurrence of violent winds and a increase of temperatures. Conventional analyses of climate series highlight significant increases of maxima and minima temperatures, although fail to show significant increases in violent winds as well as significant differences in rainfall distributions. Adaptive measures put to use by producers are many, but scarcely compensate negative effects of climate changes. Farms with little production means remain vulnerable. The study of producers' perceptions of climate changes traduces the need for a new approach in the analysis of climatic variables based on micro spatial and temporal scales

A practical approach to residential appliances on-line anomaly detection: A case study of standard and smart refrigerators

Anomaly detection is a significant application of residential appliances load monitoring systems. As an essential prerequisite of load diagnosis services, anomaly detection is critical to energy saving and occupant comfort actualization. Notwithstanding, the investigation into diagnosis of household anomalous appliances has not been decently taken into consideration. This paper presents an extensive study about operation-time anomaly detection of household devices particularly, refrigerators, in terms of appliances candidate, by utilizing their energy consumption data. Energy as a quantitative property of electrical loads, is a reliable information for a robust diagnosis. Additionally, it is very practical since it is low-priced to measure and definite to interpret. Subsequently, an on-line anomaly detection approach is proposed to effectively determine the anomalous operation of the household appliances candidate. The proposed approach is capable of continuously monitoring energy consumption and providing dynamic information for anomaly detection algorithms. A machine learning-based technique is employed to construct efficient models of appliances normal behavior with application to operation-time anomaly detection. The performance of the suggested approach is evaluated through a set of diagnostic tests, by utilizing normal and anomalous data of targeted devices, measured by an acquisition system. In addition, a comparison analysis is provided in order to further examine the effectiveness of the developed mechanism by exploiting a public database. Moreover, this study elaborates sensible remarks on an effective management of anomaly detection and diagnosis decision phases, pivotal to correctly recognition of a faulty/abnormal operation. Indeed, through experimental results of case studies, this work assists in the development of a load monitoring and anomaly detection system with practical implementation

Prevision and planning for residential agents in a transactive energy environment

Transactive Energy (TE) has brought exciting opportunities for all stakeholders in energy markets by enabling management decentralization. This new paradigm empowers demand-side agents to play a more active role through coordinating, cooperating, and negotiating with other agents. Nevertheless, most of these agents are not used to process market signals and develop optimal strategies, especially in the residential sector. Accordingly, it is indispensable to create tools that automate and facilitate demand-side participation in TE systems. This paper presents a new methodology for residential automated agents to perform two key tasks: prevision and planning. Specifically, the proposed method is applied to a forward market where agents' planning is a fundamental step to maintain the dynamic balance between demand and generation. Since planning depends on future demand, agents' prevision of consumption is an inevitable part of this step. The procedures for automating the targeted tasks are developed in a general way for residential prosumers and consumers, interacting at the distribution level. These players are managed by a demand aggregator as the leader by means of the Stackelberg game. The suggested process results in a TE setup for multi-stage single-side auctions, useful to manage future Smart Energy Markets. Through simulated transactions, this paper examines the market clearing mechanism and the convenience of agents' planning. The results show that customers with higher price-elasticity leverage lower costs periods. However, they make it harder to reduce the peak-to-average ratio of the aggregated demand profile since a unique price signal can create prisoner's dilemma conditions

Power estimation of multiple two-state loads using a probabilistic non-intrusive approach

This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability

Investigating the impact of energy source level on the self-guided vehicle system performances, in the Industry 4.0 context

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value