Rainfall-Runoff Modelling Using Artificial Neural Networks (ANNs)

Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowledge of the processes under investigation, by finding an optimum set of network parameters through the learning or training process. This thesis considers two types of ANNs, namely, self-organizing map (SOM) and feed-forward multilayer perceptron (MLP). The thesis starts with the issue of understanding of a trained ANN model by using neural interpretation diagram (NID), Garson's algorithm and a randomization approach. Then the applicability of the SOM algorithm within water resources applications is reviewed and compared to the well-known feed-forward MLP. Moreover, the thesis deals with the problem of missing values in the context of a monthly precipitation database. This part deals with the problem of missing values by using SOM and feed-forward MLP models along with inclusion of regionalization properties obtained from the SOM. The problem of filling in of missing data in a daily precipitation-runoff database is also considered. This study deals with the filling in of missing values using SOM and feed-forward MLP along with multivariate nearest neighbour (MNN), regularized expectation-maximization algorithm (REGEM) and multiple imputation (MI). Finally, once a complete database was obtained, SOM and feed-forward MLP models were developed in order to forecast one-month ahead runoff. Some issues such as the applicability of the SOM algorithm for modularization and the effect of the number of modules in modelling performance were investigated. It was found that it is indeed possible to make an ANN reveal some information about the mechanisms governing rainfall-runoff processes. The literature review showed that SOMs are becoming increasingly popular but that there are hardly any reviews of SOM applications. In the case of imputation of missing values in the monthly precipitation, the results indicated the importance of the inclusion of regionalization properties of SOM prior to the application of SOM and feed-forward MLP models. In the case of gap-filling of the daily precipitation-runoff database, the results showed that most of the methods yield similar results. However, the SOM and MNN tended to give the most robust results. REGEM and MI hold the assumption of multivariate normality, which does not seem to fit the data at hand. The feed-forward MLP is sensitive to the location of missing values in the database and did not perform very well. Based on the one-month ahead forecasting, it was found that although the idea of modularization based on SOM is highly persuasive, the results indicated a need for more principled procedures to modularize the processes. Moreover, the modelling results indicated that a supervised SOM model can be considered as a viable alternative approach to the well-known feed-forward MLP model

Flow characteristics and heat transfer performance in a Y-Fractal mini/microchannel heat sink

This article presents a combined experimental and computational study to investigate the flow and heat transfer in a Y-fractal microchannel. Experimental apparatus was newly built to investigate the effect of three different control factors, i.e., fluid flow rate, inlet temperature and heat flux, on the heat transfer characteristics of the microchannel. A standard k-Ɛ turbulence computational fluid dynamics (CFD) model was developed, validated and further employed to simulate the flow and heat transfer microchannel. A comparison between simulated results and the obtained experimental data was presented and discussed. Results showed that good agreement was achieved between the current simulated results and experimental data. Furthermore, an improved new design was suggested to further increase the heat transfer performance and create uniformity of temperature distribution.Peer reviewe

Measures of Mortality in Coronavirus (COVID-19) Compared With SARS and MERS

In late 2019, a novel coronavirus, now designated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the cause of an outbreak of acute respiratory illness in Wuhan, a city in China. Mortality rate, case fatality rate, and Years of Potential Life Lost can be measured by determining death cases. Much of our information on mortality rates of diseases can be obtained through a regular implementation of care plans that are often developed to screen infectious diseases. In the YLL component, the higher the individuals die at an earlier age, the longer their life is lost. For COVID-19, this component refers to the simple subtraction of age at death due to COVID-19 from the standardized life expectancy for the same age in the same sex. A potential application of health summary indices is to consider the non-fatal consequences of diseases to ensure that they are taken into account in health policy making. Given that COVID-19 has a non-fatal effect on a large number of patients, the estimation of disease burden using the DALYs may be an appropriate index for achieving this goal

Identification of the causes of drinking water discolouration from machine learning analysis of historical datasets

Understanding the processes and interactions occurring within complex, ageing drinking water distribution systems is vital to ensuring the supply of safe drinking water. While many water quality samples are taken for regulatory compliance, the resulting data are often simply archived rather than being interrogated for deeper understanding due to their sparse nature across time and space and the difficulties of integrating with other data sources. This paper opens a new direction of research into distribution system water quality by mining large, historical drinking water quality datasets using machine learning techniques, in this case self-organizing maps (SOMs). Application of the methodology to national-scale datasets from three different UK water companies demonstrates the ability to identify the dominant mechanisms of iron release. Factors leading to discolouration such as low disinfectant residual, nitrification, and corrosion of unlined cast iron mains were identified at scales ranging from city to country, thereby enabling targeted interventions to ensure drinking water quality

Heat transfer and entropy generation analysis of HFE 7000 based nanorefrigerants

In this study, two dimensional numerical simulations of forced convection flow of HFE 7000 based nanofluids in a horizontal circular tube subjected to a constant and uniform heat flux in laminar flow was performed by using single phase homogeneous model. Four different nanofluids considered in the present study are Al2O3, CuO, SiO2 and MgO nanoparticles dispersed in pure HFE 7000. The simulations were performed with particle volumetric concentrations of 0, 1, 4 and 6% and Reynolds number of 400, 800, 1200 and 1600. Most of the previous studies on the forced convective flow of nanofluids have been investigated through hydrodynamic and heat transfer analysis. Therefore, there is limited number of numerical studies which include both heat transfer and entropy generation investigations of the convective flow of nanofluids. The objective of the present work is to study the influence of each dispersed particles, their volume concentrations and Reynolds number on the hydrodynamic and thermal characteristics as well as the entropy generation of the flow. In addition, experimental data for Al2O3-water nanofluid was compared with the simulation model and high level agreement was found between the simulation and experimental results. The numerical results reveal that the average heat transfer coefficient augments with an increase in Reynolds number and the volume concentration for all the above considered nanofluids. It is found that the highest increase in the average heat transfer coefficient is obtained at the highest volume concentration ratio (6%) for each nanofluids. The increase in the average heat transfer coefficient is found to be 17.5% for MgO-HFE 7000 nanofluid, followed by Al2O3-HFE 7000 (16.9%), CuO-HFE 7000 (15.1%) and SiO2-HFE 7000 (14.6%). However, the results show that the enhancement in heat transfer coefficient is accompanied by the increase in pressure drop, which is about (9.3 - 28.2%). Furthermore, the results demonstrate that total entropy generation reduces with the rising Reynolds number and particle volume concentration for each nanofluid. Therefore, the use of HFE 7000 based MgO, Al2O3, CuO and SiO2 nanofluids in the laminar flow regime is beneficial and enhances the thermal performance

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

Association of fear of COVID-19 and preventive behaviors (PB) against COVID-19 in Iran

Introduction: The world is currently experiencing a pandemic of COVID-19. The pandemic may affect physicaland mental health. Therefore, this study aims to investigate the fear of COVID-19 and study the relationshipbetween fear of COVID-19 and preventive behaviors against COVID-19. Material and methods: We conducted a web-based cross-sectional study to evaluate the fear of COVID-19 andpreventive behaviors against COVID-19 among the volunteer population in Golestan Province, Iran in May 2020 andJune 2020. The online questionnaire included the Fear of COVID-19 Scale (FCV-19S) and the prevention behaviorsagainst COVID-19, which are used to assess the fear and prevention behaviors of the population, respectively.The data were presented by mean and frequency. Multiple linear regression analysis was performed to identifyfactors associated with Fear of COVID-19 at a significant level of 0.05 in Stata 14. Results: A total of 734 of the 900 individuals contacted completed the survey, with a participation rate of 81.5%.The mean age of the participants was 33.97 ± 10.68 years and 375 (51.9%) were females. The mean Fear ofCOVID-19 score in the participants was 19.69 ± 5.96. There was a significant positive correlation between Fearof COVID-19 and preventive behaviors (r = 0.19, p < 0.001). Multiple linear regression analysis showed participantswith a higher perceived threat of COVID-19, women, married participants, health workers and peoplewith underlying diseases had higher levels of fear of COVID-19. Conclusions: The fear of COVID-19 in Iranian society is high, which indicates the need to pay attention tothe mental health in pandemic conditions. Appropriate intervention action can be designed and implementedaccording to the factors that affect fear. In addition, it should be noted that people with less fear are less likelyto observe the COVID-19’s preventative behaviors

Establishing uncertainty ranges of hydrologic indices across climate and physiographic regions of the Congo River Basin

Study region The five drainage systems of the Congo River Basin in central Africa. Study focus This study aims to establish uncertainty ranges of hydrologic indices and to provide a basis for transferring hydrologic indices from gauged to ungauged sub-basins by identifying the most influential climate and physiographic attributes. New insights for this region Only limited information on individual sub-basins natural hydrology exists across the Congo River Basin, limiting the application of commonly used regionalization approaches for prediction in ungauged sub-basins. This study uses predictive equations for the hydrologic indices across all climate and physiographic regions based only on the aridity index. The degree of uncertainty in the derived uncertainty bounds is less than 41% for both Q10/MMQ and Q50/MMQ indices across the basin. A greater degree of uncertainty is associated with the runoff ratio and the Q90/MMQ indices. The uncertainty is assumed to be due to uncertainty in rainfall and evapotranspiration estimates, a lack of spatial representativeness of the available observed streamflow data and other factors (e.g., geology) that might control the hydrologic indices rather than the aridity index alone. The uncertainty ranges provide the first estimates of hydrologic indices that are intended to constrain the outputs from hydrologic models and appropriately quantify prediction uncertainty and risks associated with water resources decision making

Hydrogeochemical evolution of groundwater in a Quaternary sediment and Cretaceous sandstone unconfined aquifer in Northwestern China

A better understanding of the hydrogeochemical evolution of groundwater in vulnerable aquifers is important for the protection of water resources. To assess groundwater chemistry, groundwater sampling was performed from different representative aquifers in 2012–2013. A Piper trilinear diagram showed that the groundwater types can be classified into Na–SO4 and Na–Cl types. Only one groundwater sample was Na–HCO3 type. The dominant cations for all samples were Na+. However, the dominant anions varied from HCO3− to SO42−, and as well Cl−. The mean total dissolved solid (TDS) content of groundwater in the region was 1889 mg/L. Thus, only 20% of groundwater samples meet Chinese drinking water standards (< 1000 mg/L). Principal component analysis (PCA) combined with hierarchical cluster analysis (HCA) and self-organizing maps (SOM) were applied for the classification of the groundwater geochemistry. The three first principal components explained 58, 20, and 16% of the variance, respectively. The first component reflects sulfate minerals (gypsum, anhydrite) and halite dissolution, and/or evaporation in the shallow aquifer. The second and third components are interpreted as carbonate rock dissolution. The reason for two factors is that the different aquifers give rise to different degree of hydrogeochemical evolution (different travel distances and travel times). Identified clusters for evolution characteristic and influencing factors were confirmed by the PCA–HCA methods. Using information from eight ion components and SOM, formation mechanisms and influencing factors for the present groundwater quality were determined