Search results for: water quality classification

Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

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The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

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Authors: Isha Sharawat, R. P. Dahiya, T. R. Sreekrishnan

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One of the environmental challenges in India is urban wastewater management as regulations and infrastructural development has not kept pace with the urbanization and growing population. The quality of life of people is also improving with the rapid growth of the gross domestic product. This has contributed to the enhancement in the per capita water requirement and consumption. More domestic water consumption generates more wastewater. The scarcity of potable water is making the situation quite serious, and water supply has to be regulated in most parts of the country during summer. This requires elaborate and concerted efforts to efficiently manage the water resources and supply systems. In this article, a system dynamics modelling approach is used for estimating the water demand and wastewater generation in a district headquarter city of North India. Projections are made till the year 2035. System dynamics is a software tool used for formulation of policies. On the basis of the estimates, policy scenarios are developed for sustainable development of water resources in conformity with the growing population. Mitigation option curtailing the water demand and wastewater generation include population stabilization, water reuse and recycle and water pricing. The model is validated quantitatively, and sensitivity analysis tests are carried out to examine the robustness of the model.

Keywords: system dynamics, wastewater, water pricing, water recycle

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Masoud Rafiee

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To study the quality response of forage to chickpea-barley mixed cropping under drought stress and vermicompost consumption, an experiment was carried out under well watered and %70 water requirement (stress condition) in RCBD as split plot with four replications in temperate condition of Khorramabad in 2013. Chickpea-barley mix cropping (%100 chickpea, %75:25 chickpea:barley, %50:50 chickpea:barley, %25:75 chickpea:barley, and %100 barley) was studied. Results showed that wet and dry forage yield were significantly affected by environment and decreased in stress condition. Also, crude protein content decreased from %26.2 in well watered to %17.3 in stress condition.

Keywords: crude protein, wet forage yield, dry forage yield, water stress condition, well watered

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Keri Alhadi Ighwela

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In this research some physiochemical properties represented by temperature, pH, total hardness (TH), electrical conductivity (EC), total dissolved solids (TDS), chloride and hardness of calcium (Ca-H) and magnesium (Mg-H) were measured in the water of Ain Kaam Zliten in Libya (South side of the lagoon). A comparison of water quality with the values adopted internationally was accomplished to demonstrate the suitability for human and irrigation use. The experimental results showed that the values of pH and EC of the studied for water samples did not exceed the allowed range for drinking water. While TDS, TH, (Mg-H) and chloride values have exceeded the acceptable limit for drinking water internationally, calcium (Ca-H) results have shown a decrease in values of all samples except the first sample which record a marginal increase.

Keywords: physiochemical properties, Ain Kaam lagoon, Zliten, Libya

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Andrey V. Timofeev

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This paper introduces an original method for guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with a probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is, the higher is the classification accuracy. Experiments have shown that if the cardinality of the classifiers ensemble is increased then the cardinality of this set of hypothetical classes is reduced. The problem of the guaranteed estimation of the accuracy of an ensemble of Lipschitz classifiers is relevant in the multichannel classification of target events in C-OTDR monitoring systems. Results of suggested approach practical usage to accuracy control in C-OTDR monitoring systems are present.

Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble

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Authors: Yusra Mahfooz, Saleha Mehmood

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With increase in industrialization and urbanization, water contaminating rivers through effluents laden with diverse chemicals in developing countries. The study was based on the waste water quality of the four drains (Outfall, Gulshan -e- Ravi, Hudiara, and Babu Sabu) which enter into river Ravi in Lahore, Pakistan. Different pollution parameters were analyzed including pH, DO, BOD, COD, turbidity, EC, TSS, nitrates, phosphates, sulfates and fecal coliform. Approximately all the water parameters of drains were exceeded the permissible level of wastewater standards. In calculation of pollution load, Hudiara drains showed highest pollution load in terms of COD i.e. 429.86 tons/day while in Babu Sabu drain highest pollution load was calculated in terms of BOD i.e. 162.82 tons/day (due to industrial and sewage discharge in it). Lab scale treatment (oxidation ponds) was designed in order to treat the waste water of Babu Sabu drain, through combination of different algae species i.e. chaetomorphasutoria, sirogoniumsticticum and zygnema sp. Two different sizes of ponds (horizontal and vertical), and three different concentration of algal samples (25g/3L, 50g/3L, and 75g/3L) were selected. After 6 days of treatment, 80 to 97% removal efficiency was found in the pollution parameters. It was observed that in the vertical pond, maximum reduction achieved i.e. turbidity 62.12%, EC 79.3%, BOD 86.6%, COD 79.72%, FC 100%, nitrates 89.6%, sulphates 96.9% and phosphates 85.3%. While in the horizontal pond, the maximum reduction in pollutant parameters, turbidity 69.79%, EC 83%, BOD 88.5%, COD 83.01%, FC 100%, nitrates 89.8%, sulphates 97% and phosphates 86.3% was observed. Overall treatment showed that maximum reduction was carried out in 50g algae setup in the horizontal pond due to large surface area, after 6 days of treatment. Results concluded that algae-based treatment are most energy efficient, which can improve drains water quality in cost effective manners.

Keywords: oxidation pond, ravi pollution, river water quality, wastewater treatment

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Authors: R. L. P. de Lima, F. C. B. Boogaard, D. Setyo Rini, P. Arisandi, R. E. de Graaf-Van Dinther

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Water quality in many Asian countries is very poor due to inefficient solid waste management, high population growth and the lack of sewage and purification systems for households and industry. A consortium of Indonesian and Dutch organizations has begun a large-scale international research project to evaluate and propose solutions to face the surface water pollution challenges in Brantas Basin, Indonesia (East Java: Malang / Surabaya). The first phase of the project consisted in a baseline study to assess the current status of surface water bodies and to determine the ambitions and strategies among local stakeholders. This study was conducted with high participatory / collaborative and knowledge sharing objectives. Several methods such as using mobile sensors (attached to boats or underwater drones), test strips and mobile apps, bio-monitoring (sediments), ecology scans using underwater cameras, or continuous / static measurements, were applied in different locations in the regions of the basin, at multiple locations within the water systems (e.g. spring, upstream / downstream of industry and urban areas, mouth of the Surabaya River, groundwater). Results gave an indication of (reference) values of basic water quality parameters such as turbidity, electrical conductivity, dissolved oxygen or nutrients (ammonium / nitrate). An important outcome was that collecting random samples may not be representative of a body of water, given that water quality parameters can vary widely in space (x, y, and depth) and time (day / night and seasonal). Innovative / dynamic monitoring methods (e.g. underwater drones, sensors on boats) can contribute to better understand the quality of the living environment (water, ecology, sediment) and factors that affect it. The field work activities, in particular, underwater drones, revealed potential as awareness actions as they attracted interest from locals and local press. This baseline study involved the cooperation with local managing organizations with Dutch partners, and their willingness to work together is important to ensure participatory actions and social awareness regarding the process of adaptation and strengthening of regulations, or for the construction of facilities such as sewage.

Keywords: water quality monitoring, pollution, underwater drones, social awareness

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Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali

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Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.

Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile

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Authors: Davidson E. Egirani, Nanfe R. Poyi, Napoleon Wessey

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This paper would report on the effect of solid waste on water resource quality in the Gbarain catchment of the Niger Delta Region of Nigeria. The Gbarain catchment presently hosts two waste-dump sites located along the flanks of a seasonal flow stream and perennially waterlogged terrain. The anthropogenic activity has significantly affected the quality of surface and groundwater in the Gbarain catchment. These wastes have made the water resource environment toxic leading to the poisoning of aquatic life. The contaminated water resources could lead to serious environmental and human health challenges such as low agricultural yields to loss of vital human organs. The contamination is via geological processes such as seepage and direct infiltration of contaminants into watercourses. The results obtained from field and experimental investigations followed by modeling, and graphical interpretation indicate heavy metal load and fecal pollution in some of the groundwater. The metal load, Escherichia coli, and total coliforms counts exceed the international and regional recommended limits. The contaminate values include Lead (> 0.01 mg/L), Mercury (> 0.006 mg/L), Manganese (> 0.4 mg/L and Escherichia coli (> 0 per 100ml) of the samples. Land use planning, enactment, and implementation of environmental laws are necessary for this region, for effective surface water and groundwater resource management.

Keywords: aquatic life, solid waste, environmental health, human health, waste-dump site, water-resource environment

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Authors: Asnakew Mulualem Tegegne, Tarun Kumar Lohani, , Abunu Atlabachew Eshete

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Groundwater quality has become deteriorated due to natural and anthropogenic activities. Poor water quality has a potential risk to human health and the environment. Therefore, the study aimed to assess the potential risk of groundwater quality contamination levels and public health risks in the Gunabay watershed. For this task, seventy-eight groundwater samples were collected from thirty-nine locations in the dry and wet seasons during 2022. The ground water contamination index was applied to assess the overall quality of groundwater. Six major driving forces (temperature, population density, soil, land cover, recharge, and geology) and their quantitative impact of each factor on groundwater quality deterioration were demonstrated using Geodetector. The results showed that low groundwater quality was detected in urban and agricultural land. Especially nitrate contamination was highly linked to groundwater quality deterioration and public health risks, and a medium contamination level was observed in the area. This indicates that the inappropriate application of fertilizer on agricultural land and wastewater from urban areas has a great impact on shallow aquifers in the study area. Furthermore, the major influencing factors are ranked as soil type (0.33–0.31)>recharge (0.17–0.15)>temperature (0.13–0.08)>population density (0.1–0.08)>land cover types (0.07– 0.04)>lithology (0.05–0.04). The interaction detector revealed that the interaction between soil ∩ recharge, soil ∩ temperature, and soil ∩ land cover, temperature ∩ recharge is more influential to deteriorate groundwater quality in both seasons. Identification and quantification of the major influencing factors may provide new insight into groundwater resource management.

Keywords: groundwater contamination index, geographical detectors, public health · influencing factors, and water resources management

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Authors: Nor Fazlina Iryani Abdul Hamid, Mohamad Khatim Hasan

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Evolution of software quality measurement has been started since McCall introduced his quality model in year 1977. Starting from there, several software quality models and software quality measurement methods had emerged but none of them focused on telecommunication industry. In this paper, the implementation of software quality measurement system for telecommunication industry was compulsory to accommodate the rapid growth of telecommunication industry. The quality value of the telecommunication related software could be calculated using this system by entering the required parameters. The system would calculate the quality value of the measured system based on predefined quality metrics and aggregated by referring to the quality model. It would classify the quality level of the software based on Net Satisfaction Index (NSI). Thus, software quality measurement system was important to both developers and users in order to produce high quality software product for telecommunication industry.

Keywords: software quality, quality measurement, quality model, quality metric, net satisfaction index

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Authors: Zainab Abbas Soharwardi, Chunli Su, Fazeelat Tahira, Syed Zahid Aziz

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The study monitors the quality of drinking water consumed by urban population of Lahore. A total of 50 drinking water samples (16 from source and 34 from distribution system) were examined for physical, chemical and bacteriological parameters. The parameters including pH, turbidity, electrical conductivity, total dissolved solids, total hardness, calcium, magnesium, total alkalinity, carbonate, sulphate, chloride, nitrite, fluoride, sodium and potassium were analyzed. Sixteen out of fifty samples showed high values of alkalinity compared to EPA standards and WHO guidelines. Twenty-eight samples were analyzed for heavy metals, chromium, iron, copper, zinc, cadmium and lead. Trace amounts of heavy metals were detected in some samples, however for most of the samples values were within the permissible limits although high concentration of zinc was detected in one sample collected from Mughal Pura area. Fifteen samples were analyzed for arsenic. The results were unsatisfactory; around 73% samples showed exceeding values of As. WHO has suggested permissible limits of arsenic < 0.01 ppm, whereas 27 % of samples have shown 0.05 ppm arsenic, which is five times greater than WHO highest permissible limits. All the samples were examined for E. coli bacteria. On the basis of bacteriological analysis, 42 % samples did not meet WHO guidelines and were unsafe for drinking.

Keywords: arsenic, heavy metals, ground water, Lahore

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Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

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This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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Authors: Agung Rizki Perdana, Asrofi Mursalin, Adniwan Shubhi Banuzaki, M. Indra Novian

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The understanding about sediment-derived plastiglomerate has a wide-range of merit in the academic realm. It can cover discussions about the Anthropocene Epoch in the scope of geoscience knowledge to even provide a solution for the environmental problem of plastic waste. Albeit its importance, very few research has been done regarding this issue. This research aims to create a classification as a pioneer for the study of sediment-derived plastiglomerate. This research was done in Bantul Regency, Daerah Istimewa Yogyakarta Province as an analogue of plastic debris sedimentation process. Observation is carried out in five observation points that shows three different depositional environments, which are terrestrial, fluvial, and transitional environment. The resulting classification uses three parameters and forms in a taxonomical manner. These parameters are composition, degree of lithification, and abundance of matrix respectively in advancing order. There is also a compositional ternary diagram which should be followed before entering the plastiglomerate nomenclature classification.

Keywords: plastiglomerate, classification, sedimentary mechanism, microplastic

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Authors: Prasanna Haddela

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Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

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Authors: Sima Saadi, Carolyn Johns

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The concept of a "Smart Water City" is emerging as a framework to address critical urban water challenges, integrating technology, data, and sustainable management practices to enhance water quality, conservation, and accessibility. This paper explores the definition of a Smart Water City, examines the pressing need for such cities in Canada, and proposes policy pathways for their development. Smart Water Cities utilize advanced monitoring systems, data analytics, and integrated water resources management to optimize water usage, anticipate and mitigate environmental impacts, and engage citizens in sustainable practices. Global examples from regions such as Europe, Asia, and Australia illustrate how Smart Water City models can transform urban water systems by enhancing resilience, improving resource efficiency, and driving economic development through job creation in environmental technology sectors. For Canada, adopting Smart Water City principles could address pressing challenges, including climate-induced water stress, aging infrastructure, and the need for equitable water access across diverse urban and rural communities. Building on Canada's existing water policies and technological expertise, it propose strategic investments in digital water infrastructure, data-driven governance, and community partnerships. Through case studies, this paper offers insights into how Canadian cities could benefit from cross-sector collaboration, policy development, and funding for smart water technology. By aligning national policy with smart urban water solutions, Canada has the potential to lead globally in sustainable water management, ensuring long-term water security and environmental stewardship for its cities and communities.

Keywords: smart water city, urban water resilience, water management technology, sustainable water infrastructure, canada water policy, smart city initiatives

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Authors: Wehandaka Pancapalaga

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This study aimed to analyze the water content, yield, fat content, protein content, viscosity, gel strength, pH, melting and organoleptic rabbit skin gelatin with acetic acid extraction levels are different. The materials used in this study were Rex rabbit skin male. Treatments that P1 = the extraction of acetic acid 2% (v / v); P2 = the extraction of acetic acid 3% (v / v); P3 = the extraction of acetic acid 4 % (v / v). P5 = the extraction of acetic acid 5% (v / v). The results showed that the greater the concentration of acetic acid as the extraction of rabbit skin can reduce the water content and fat content of rabbit skin gelatin but increase the protein content, viscosity, pH, gel strength, yield and melting point rabbit skin gelatin. texture, color and smell of gelatin rabbits there were no differences with cow skin gelatin. The results showed that the quality of rabbit skin gelatin accordance Indonesian National Standard (SNI). Conclusion 5% acetic acid extraction produces the best quality gelatin.

Keywords: gelatin, skin rabbit, acetic acid extraction, quality

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Authors: Imessaoudene Y., Mouhouche B., Sengouga A., Kadir M.

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The objective of this work is to determine the virtual water of main crops grown in the province of Djelfa and water use efficiency (W.U.E.), Which is essential to approach the application and better integration with the offer in the region. In the case of agricultural production, virtual water is the volume of water evapo-transpired by crops. It depends on particular on the expertise of its producers and its global production area, warm and dry climates induce higher consumption. At the scale of the province, the determination of the quantities of virtual water is done by calculating the unit water requirements related to water irrigated hectare and total rainfall over the crop using the Cropwat 8.0 F.A.O. software. Quantifying the volume of agricultural virtual water of crops practiced in the study area demonstrates the quantitative importance of these volumes of water in terms of available water resources in the province, so the advantages which can be the concept of virtual water as an analysis tool and decision support for the management and distribution of water in scarcity situation.

Keywords: virtual water, water use efficiency, water requirements, Djelfa

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Authors: Lina Lukusa, Ulrike Rivett

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Poor water management can increase the extreme pressure already faced by water scarcity. Unless water management is addressed holistically, water quality and quantity will continue to degrade. A holistic approach to water management named Water Sensitive Urban Design (WSUD) has thus been created to facilitate the effective management of water. Traditionally, water management has employed a linear design approach, while WSUD requires a systematic, cyclical approach. In simple terms, WSUD assumes that everything is connected. Hence, it is critical for different stakeholders involved in WSUD to engage and reach a consensus on a solution. However, many stakeholders in WSUD have conflicting interests. Using the soft system methodology (SSM), developed by Peter Checkland, as a problem-solving method, decision-makers can understand this problematic situation from different world views. The SSM addresses ill and complex challenging situations involving human activities in a complex structured scenario. This paper demonstrates how SSM can be applied to understand the complexity of stakeholder engagement in WSUD. The paper concludes that SSM is an adequate solution to understand a complex problem better and then propose efficient solutions.

Keywords: co-design, ICT platform, soft systems methodology, water sensitive urban design

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Authors: Jesús Horacio, Alfredo Ollero, Víctor Bouzas-Blanco, Augusto Pérez-Alberti

Abstract:

Rivers have traditionally been classified, assessed and managed in terms of hydrological, chemical and / or biological criteria. Geomorphological classifications had in the past a secondary role, although proposals like River Styles Framework, Catchment Baseline Survey or Stroud Rural Sustainable Drainage Project did incorporate geomorphology for management decision-making. In recent years many studies have been attracted to the geomorphological component. The geomorphological processes and their associated forms determine the structure of a river system. Understanding these processes and forms is a critical component of the sustainable rehabilitation of aquatic ecosystems. The fluvial auto-classification approach suggests that a river is a self-built natural system, with processes and forms designed to effectively preserve their ecological function (hydrologic, sedimentological and biological regime). Fluvial systems are formed by a wide range of elements with multiple non-linear interactions on different spatial and temporal scales. Besides, the fluvial auto-classification concept is built using data from the river itself, so that each classification developed is peculiar to the river studied. The variables used in the classification are specific stream power and mean grain size. A discriminant analysis showed that these variables are the best characterized processes and forms. The statistical technique applied allows to get an individual discriminant equation for each geomorphological type. The geomorphological classification was developed using sites with high naturalness. Each site is a control point of high ecological and geomorphological quality. The changes in the conditions of the control points will be quickly recognizable, and easy to apply a right management measures to recover the geomorphological type. The study focused on Galicia (NW Spain) and the mapping was made analyzing 122 control points (sites) distributed over eight river basins. In sum, this study provides a method for fluvial geomorphological classification that works as an open and flexible tool underlying the fluvial auto-classification concept. The hydrographic mapping is the visual expression of the results, such that each river has a particular map according to its geomorphological characteristics. Each geomorphological type is represented by a particular type of hydraulic geometry (channel width, width-depth ratio, hydraulic radius, etc.). An alteration of this geometry is indicative of a geomorphological disturbance (whether natural or anthropogenic). Hydrographic mapping is also dynamic because its meaning changes if there is a modification in the specific stream power and/or the mean grain size, that is, in the value of their equations. The researcher has to check annually some of the control points. This procedure allows to monitor the geomorphology quality of the rivers and to see if there are any alterations. The maps are useful to researchers and managers, especially for conservation work and river restoration.

Keywords: fluvial auto-classification concept, mapping, geomorphology, river

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Authors: Aishath Shakeela

Abstract:

At a global level, water stewardship, water stress and water security are crucial factors in tourism planning and development considerations. Challenges associated with water is of particular concern to the Maldives as there is limited availability of freshwater, high dependency on desalinated water, and high unit cost associated with desalinating water. While the Maldives is promoted as an example of sustainable tourism, a key sustainability challenge facing tourism dependent communities is the efficient use and management of available water resources. A water crisis event in the capital island of Maldives highlighted how precarious water related issues are in this tourism dependent destination. Applying netnography, the focus of this working paper is to present community perceptions of how government policies addressed Malé Water and Sewerage Company (MWSC) water crisis event.

Keywords: crisis management, government policies, Maldives, tourism, water

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Authors: Nahed M. M. Ismail, Bayoumy B. Mostafa, Ahmed Abdel-Kader, Khalil M. El-Said, Asmaa Abdel-Motleb, Hoda M. Abu Taleb

Abstract:

This study was carried out in lined and unlined watercourses in Beheira and Giza governorates to investigate the effect of water canals lining on water quality and aquatic vegetations. Samples of water and aquatic plants were collected from the examining sites during four seasons in two successive years. The main ecological parameters were recorded and water quality was measured. Results showed that the mean value of water conductivity and total dissolved salts in lined sites was significantly lower than those of unlined ones (p < 0.01, p < 0.05). In Beheira, the dissolved oxygen concentrations during autumn and winter were higher in lined sites (3.93±1.3 and 9.6±1.1 ppm, respectively) than those of unlined ones (the same values of 1.2±0.6 ppm). However, it represented by lower values of 5.77±6.05 and 4.9±1.8 ppm in lined watercourses in spring and summer, respectively, comparing with those in unlined ones (14.05±5.59 and 5.83±0.8 ppm, respectively). Generally, Zn, Pb, Fe, Cd were higher in both lined and unlined sites during summer than the other seasons. However, Zn and Fe were higher in lined sites (0.78±0.37 and 17.4±4.3 ppb, respectively) during summer than that of unlined ones (0.4±0.1 and 10.95±1.93 ppb, respectively). Cu was absent during summer in lined and unlined sites and only in unlined ones during spring. Regarding to Giza sites, Cu and Pb were absent in both lined and unlined sites during summer and only in unlined ones during spring. Whereas, Fe recorded higher values in autumn in both lined (8.8±20.1 ppb) and unlined sites (15.16±3 ppb) than the other seasons. Present survey study revealed that 13 species of aquatic plants were collected from lined and unlined sites in Beheira and Giza governorates. Eichhornia crassipes, Ceratophyllum demersum, and Potamogeton sp. were the only plant species infested the examined sites during autumn and winter in Beheira. In autumn C. demersum was the only plant found in lined sites represented by highly lower significant percentage (12.5% of the all examined sites) compared to the unlined sites (50%). E. crassipes was completely absent in the lined sites during the two seasons. In spring, there is only 3 plant species in lined sites compared to 6 ones in unlined. Also, in summer, there is only 2 species in lined sites comparing with 5 in unlined. The percentage of occurrence and density of these plants was highly significant (p < 0.01, p < 0.001) higher in unlined sites compared to the lined ones during all seasons. A diversity of plant species, E. crassipes, C. demersum, Jussias repens, Lemma giba, and Polygonum serr were the most abundant in many examined sites during all seasons in Giza. In summer, the percentage of sites containing the two plants E. crassipes (83.3%) and C. demersum (50%) was highly significant (p < 0.001) higher in unlined sites compared to the lined ones (50% and 0.0%, respectively). It concluded from the results that watercourses lining may play a significant role in preserving water with a good quality and reduces the distribution of aquatic vegetation which rendered the current of water.

Keywords: aquatic plants, lining of watercourses, physicochemical parameters, water quality

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

Abstract:

In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

Procedia PDF Downloads 79