Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model’s performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model’s incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data.
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).
Beekeeping in Africa has been practiced for many years through successive generations and along inherited patterns. Beekeepers continue to face challenges in accessing consistent and business-driven markets for their bee products. In addition, the honeybee populations are decreasing due to colony collapse disorder (CCD), fire, loss of bees in swarming, honey buggers and other animals, moths, starvation, cold weather, and Varoa mites. The main issues are related to un-controlled temperature, humidity, and traditional management of beekeeping. These challenges result in low production of honey and colony losses. The control of the environmental conditions within and surrounding the beehives are not available to beekeepers due to the lack of monitoring systems. A Smart Beehive System using Internet of Things (IoT) technology would allow beekeepers to keep track of the amount of honey created in their hives and bee colonies even when they are far from their hives, through mobile phones, which would curtail the challenges currently faced by the beekeepers. However, there are challenges in the design of energy-efficient embedded electronic devices for IoT. A promising solution is to provide energy autonomy to the IoT nodes that will harvest residual energy from ambient sources, such as motion, vibrations, light, or heat. This paper proposes a Self-Powered Smart Beehive Monitoring and Control System (SBMaCS) using IoT to support remote follow-up and control, enhancing bee colonies’ security and thus increasing the honey productivity. First, we develop the SBMaCS hardware prototype interconnecting various sensors, such as temperature sensor, humidity sensor, piezoelectric transducer—which will work as a weight sensor—motion sensor, and flame sensor. Second, we introduce energy harvesting models to self-power the SBMaCS by analyzing the (i) energy harvested from adult bees’ vibrations, (ii) energy harvesting through the piezoelectric transducer, and (iii) radio frequency energy harvesting. Third, we develop a mobile phone application that interacts with the SBMaCS hardware to monitor and control the various parameters related to the beehives. Finally, the SBMaCS PCB layout is also designed. SBMaCS will help beekeepers to successfully monitor and control some important smart beekeeping activities wherever they are using their mobile phone application.
The rapid expansion of Internet of Things (IoT) devices and applications has accelerated research in various areas of human development. However, the cost of commercial instrumentation impedes the momentum of technological growth in developing regions. In this study, a low-cost, low-power, wireless sensor network for groundwater monitoring (LWNGM) was developed to provide near real-time groundwater level data to support prudent decision making in groundwater resource management in Zanzibar, Tanzania. To facilitate reproducibility, we provided a detailed description of the LWNGM development procedure. The system is based on the ATmega328P microcontroller platform and incorporates low-cost MS5803-14BA and MB280 sensors. To provide a low-power scheme, the Arduino UNO wakes up in six-hour intervals for measurements and data-logging to the SD card, and at twelve-hour intervals for relaying data (in batches) to the LoRa gateway, before it goes back into a deep-sleep mode for the rest of the time (duty cycle<1% ). The average power consumption for the end node across all system cycles was 104.081mW. The power autonomy of all nodes is provided by a 3.7V, 5000mAh rechargeable LiPo battery, and a 9V, 600mAh rechargeable Liion battery, respectively, which are supported by 6V and, 3W solar chargers. The data processing and storage components, as well as the data visualization dashboard, were created using free and open-source software. The LWNGM was reasonably priced, ranging between $350 and $400. The proposed system allows for the adoption of hydrological monitoring, particularly in areas with a limited budget for hydrologic management.
The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.
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