The main objective of this paper is to experimentally assess the real-life outdoor performance of a photovoltaic-thermal (PVT) module against a conventional photovoltaic (PV) system in a hot humid tropical climate in Ghana. An experimental setup comprising a water-based mono-crystalline silicon PVT and an ordinary mono-crystalline silicon PV was installed on a rooftop at the Kwame Nkrumah University of Science and Technology in Kumasi and results evaluated for the entire year of 2019. It was observed that the annual total output energy of PV module was 194.79 kWh/m² whereas that of the PVT for electrical and thermal outputs were 149.92 kWh/m² and 1087.79 kWh/m², respectively. The yearly average daily electrical energy yield for the PV and PVT were 3.21 kWh/kW_p/day and 2.72 kWh/kW_p/day, respectively. The annual performance ratios for the PV and PVT (based on electrical energy output only) were 79.2% and 51.6%, respectively, whilst their capacity factors were, respectively, 13.4% and 11.3%. Whereas the highest monthly mean efficiency recorded for the PV was 12.7%, the highest combined measured monthly mean electrical/thermal efficiency of the PVT was 56.1%. It is also concluded that the PVT is a worthy prospective alternative energy source in off-grid situations.

Dengue fever is a mosquito-borne disease accounting for 50–100 million annual cases globally. Laos and Thailand are countries in south-east Asia where the disease is endemic in both urban and rural areas. Household water storage containers, which are favourable breeding sites for dengue mosquitoes, are common in these areas, due to intermittent or limited access to water supply. This study assessed the effect of household water management and socio-demographic risk factors on Aedes aegypti infestation of water storage containers. A cross-sectional survey of 239 households in Laos (124 suburban and 115 rural), and 248 households in Thailand (127 suburban and 121 rural) was conducted. Entomological surveys alongside semi-structured interviews and observations were conducted to obtain information on Ae. aegypti infestation, socio-demographic factors and water management. Zero-inflated negative binomial regression models were used to assess risk factors associated with Ae. aegypti pupal infestation. Household water management rather than socio-demographic factors were more likely to be associated with the infestation of water containers with Ae. aegypti pupae. Factors that was significantly associated with Ae. aegypti infestation were tanks, less frequent cleaning of containers, containers without lids, and containers located outdoors or in toilets/bathrooms. Associations between Ae. aegypti pupae infestation, household water management, and socio-demographic factors were found, with risk factors for Ae. aegypti infestation being specific to each study setting. Most of the containers did not have lids, larvicides, such as temephos was seldom used, and containers were not cleaned regularly; factors are facilitating dengue vector proliferation. It is recommended that, in Lao villages, health messages should promote proper use and maintenance of tightly fitted lids, and temephos in tanks, which were the most infested containers. Recommendations for Thailand are that small water containers should be cleaned weekly. Furthermore, in addition to health messages on dengue control provided to communities, attention should be paid to larval control for indoor containers in rural villages. Temephos or other immature control measures such as the use of pyriproxyfen, antilarval bacteria, or larvivorous fish should be used where temephos resistance is prevalent. Dengue control is not possible without additional adult mosquito control and community participation.

Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been proposed as a robust technique for predicting discharges in wastewater networks. This study explored the potential application of an LSTM model to predict discharges using 3-month data set in a sewer network in Ålesund city, Norway. Different sequence-to-sequence LSTMs were investigated using various input and output datasets. The impact of data aggregation (10-min and 30-min intervals) was examined and compared to original sensor data (5-min intervals) to evaluate the performance of the LSTM model. The results show that 50-neuron LSTM architecture performed better (MAPE = 0.09, RMSE = 0.0008, R² = 0.8) in predicting discharges for the study area. The study indicates that using the same sequence length for the prior and the forecast can improve the effectiveness of the LSTM model. Based on the results, using a 10-min aggregated discharge dataset reduces energy consumption, transmission bandwidth, and storage capacity. Additionally, it improves prediction performance compared to an original 5-min interval data in Ålesund city.

Predicting the condition of sewer pipes plays a vital role in the formulation of predictive maintenance strategies to ensure the efficient renewal of sewer pipes. This study explores the potential application of ten machine learning (ML) algorithms to predict sewer pipe conditions in Ålesund, Norway. Ten physical factors (age, diameter, depth, slope, length, pipe type, material, network type, pipe form, and connection type) and ten environmental factors (rainfall, geology, landslide area, population, land use, building area, groundwater, traffic volume, distance to road, and soil type) were used to develop the ML models. The filter, wrapper, and embedded methods were used to assess the significance of the input factors. A dataset consisting of 1159 inspected sewer pipes was used to construct the sewer condition models, and 290 remaining inspections were used to verify the models. The results showed that sewer material and age are the most significant factors, otherwise the network type is the least contributor affecting the sewer conditions in the study area. Among the considered ML models, the Extra Trees Regression (R² = 0.90, MAE = 11.37, and RMSE = 40.75) outperformed the other ML models and it is recommended for predicting sewer conditions for the study area. The results of this study can support utilities and relevant agencies in planning predictive maintenance strategies for their sewer networks.

Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide a timely response to changes in water quality. This research addresses this issue by proposing a data-driven solution that enhances the timeliness of water quality monitoring. Our research team applied a case study in Ålesund Kommune. A sensor platform has been deployed at Lake Brusdalsvatnet, the water source reservoir in Ålesund. This sensor module is capable of collecting data for 10 different physico-chemical indicators of water quality. Leveraging this sensor platform, we developed a CNN-AutoEncoder-SOM solution to automatically monitor, process, and evaluate water quality evolution in the lake. There are three components in this solution. The first one focuses on anomaly detection. We employed a recurrence map to encode the temporal dynamics and sensor correlations, which were then fed into a convolutional neural network (CNN) for classification. It is noted that this network achieved an impressive accuracy of up to $99.6\%$. Once an anomaly is detected, the data are calibrated in the second component using an AutoEncoder-based network. Since true values for calibration are unavailable, the results are evaluated through data analysis. With high-quality calibrated data in hand, we proceeded to cluster the data into different categories to establish water quality standards in the third component, where a self-organizing map (SOM) is applied. The results revealed that this solution demonstrated significant performance, with a silhouette score of 0.73, which illustrates a small in-cluster distance and large intra-cluster distance when the water was clustered into three levels. This system not only achieved the objective of developing a comprehensive solution for continuous water quality monitoring but also offers the potential for integration with other cyber–physical systems (CPSs) in urban water management.