Reliable seasonal prediction of groundwater levels is not always possible when the quality and the amount of available on-site groundwater data are limited. In the present work, a hybrid K-Nearest Neighbor-Random Forest (KNN-RF) is used for the prediction of variations in groundwater levels (L) of an aquifer with the groundwater relatively close to the surface (<10 m) is proposed. First, the time-series smoothing methods are applied to improve the quality of groundwater data. Then, the ensemble K-Nearest Neighbor-Random Forest (KNN-RF) model is treated using hydro-climatic data for the prediction of variations in the levels of the groundwater tables up to three months ahead. Climatic and groundwater data collected from eastern Rwanda were used for validation of the model on a rolling window basis. Potential predictors were: the observed daily mean temperature (T), precipitation (P), and daily maximum solar radiation (S). Previous day’s precipitation P (t − 1), solar radiation S (t), temperature T (t), and groundwater level L (t) showed the highest variation in the fluctuations of the groundwater tables. The KNN-RF model presents its results in an intelligible manner. Experimental results have confirmed the high performance of the proposed model in terms of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe (NSE), and coefficient of determination (R2).

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Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.

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The impact of COVID-19 has been felt across all sectors, from transportation, education, and public works to the daily operations of businesses like selling, retailing, and so forth. The business sector is among those badly affected, especially micro, small, and medium enterprises. The understanding of ground prevailing conditions is key in driving informed policies that would have meaningful impact on society with regard to overcoming the effects of the virus. Hence, this work is an attempt to report the real ground statistics and necessity of technological support with the goal of submitting a report of recommended policies to the concerned authorities. In this direction, this work presents the outcome of a survey conducted to assess the impact of COVID-19 on operations of micro, small, and medium enterprises and also to find out the interventions put in place around business environments so as to enforce adherence to COVID-19 health safety measures. The survey was part of a study to develop automated IoT-powered technological solutions that would help to enforce proper mask wearing in indoor environments and also observance of social distance requirements within business premises. A customized questionnaire was designed to capture data on various aspects central to the focus of the study. The study was carried out in the month of May 2021, in the Huye district of Rwanda. According to the survey findings, the major challenges faced by businesses due to COVID-19 include failure by clients to settle bills, reduced ability to expand investment, difficulty in accessing inputs domestically, lower domestic sales to consumers, and lower domestic sales to businesses. The results also reveal some positive points that most businesses were found to have: hand washing points, hand sanitizer dispensers, and mechanisms to enforce social distance between customer and customer and also customer and front desk worker. In a nutshell, this work is unique in terms of (1) the customized questionnaire about Rwanda’s needs, (2) field visit-based data collection for accurate data, and (3) including an assessment of the importance of technological intervention for better handling of public safety, especially in the MSME business sector.

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