Hierarchical Clustering Technique Research Articles

Targeting interventions for HIV testing and treatment uptake: An attitudinal and behavioural segmentation of men aged 20–34 in KwaZulu-Natal and Mpumalanga, South Africa

Despite recent improvements, men still have worse HIV outcomes than women in South Africa. This study describes how young men form distinct behavioural and attitudinal subgroups, and is intended to inform the design of targeted interventions to encourage HIV testing and initiation on antiretroviral therapy. Data were collected using a cross-sectional survey with questions on men’s attitudes, beliefs and behaviours around HIV/AIDS. A total of 2,019 men were randomly sampled from eight district municipalities in KwaZulu-Natal and Mpumalanga provinces between October 2018 and January 2019. Men were eligible to participate if they were aged 20–34, Black African, had an education level below university graduation, were aware of HIV and were willing to disclose whether they had tested for HIV. Each participant responded to a questionnaire asking about their demographic characteristics, reported sexual behaviour, engagement with HIV testing and treatment services, alcohol consumption, HIV knowledge, attitudes to gender equity and reported level of depressive symptoms. Data were analysed using canonical correlation, hierarchical clustering and factor analysis techniques to produce five groups of men. The results were synthesised using Human Centred Design principles to suggests areas for potential intervention for each segment. The results showed that men vary based on their attitudes to gender and masculinity, use of alcohol, testing and treatment behaviour, HIV-related fears and preferences for testing modalities. Segment 1 (21%) avoids the topic of HIV, perhaps fearful of the impact on his life. Segment 2 (23%) is well connected to his community and has social concerns about HIV. Segment 3 (15%) struggles with more distal determinants of HIV acquisition such as unemployment and poor mental health. Segment 4 (25%) has concerns about the lifestyle changes that would be required if he were HIV positive. Segment 5 (16%) has a strong traditional mindset and is fearful of the ramifications of HIV in his community. The results will be used to design targeted interventions to increase HIV testing and treatment rates among young men in South Africa. Further research is required to understand the impact of interventions designed in this way.

Detection of Alternaria alternata in tomato juice and fresh fruit by the production of its biomass, respiration, and volatile compounds

Tomato is widely consumed and marketed as juice, puree, or fresh product. Nevertheless, 30% of its harvest volume is lost because of the fungus Alternaria alternata. This research aimed to provide early detection methods for this fungal decay on tomato juice and fresh fruit. Biomass content, CO2, O2 and volatile compounds (VOCs) during A. alternata growth in tomato juice and fruit at two ripening stages (breaker and red colour) were evaluated. Additionally, CO2 and VOCs data set were analysed with a hierarchical cluster technique (HCA) to explore the differences between inoculated and non-inoculated samples. Biomass was determined by gravimetry, CO2 and O2 by gas chromatography (GC), and VOCs by GC-mass spectrometry. Biomass content was not drastically modified by tomato's ripening stage (3–6 mg of dry weight). CO2 in tomato juice was considerably higher in the inoculated samples with A. alternata (27–63%) than in the non-inoculated ones (2.8–6.6%), regardless of the ripeness stage; while in tomato fruit CO2 was higher at breaker stage and inoculated with A. alternata (33–41%) than the remaining treatments (9–23%). It was also observed that, except for limonene, trans-sabinene hydrate, and rhodovibrin, VOCs' release during the interaction between tomato juice and A. alternata was different from the fresh tomato and A. alternata interaction. Only the HCA based on CO2 data showed clear differences between the inoculated and non-inoculated tomato juice and fruit at both ripening stages.

Predicting User’s Web Navigation behaviour using AMD and HMM Approaches

The academics were introducing various techniques in web usage mining to reduce user latency time to improve their web performance. The server-side web log data helps to identify the most appropriate pages, based on the user’s request. Analyzing web log data creates difficulties since it comprises exhaustive web page data. This paper proposes a new technique in which the web log data we can preprocess in order to extract sequence and navigation patterns useful to predict. In this paper, we cluster users into communities labelled by the websites most frequently visited, to discover their preferences and to describe website reorganisation. The URL sequence navigated by a user during a session. Session denotes a user’s browsing pattern. Multiple users’ sessions are clustered using the hierarchical clustering technique to analyse the sequence in their navigation patterns. The two model predictions are Adaptive Mahanabolis Distance (AMD) and Hidden Markov model (HMM), providing a list of web pages of interest to the user. We validate the proposed framework on NASA, Clarknet and serc datasets web log files.

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients’ active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.

UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil

Sodicity is a major soil constraint in many arid and semi-arid regions worldwide, including Australia, which adversely affects the ability of crops to take up water and nutrients from the soil, reducing yield. Reliable methods and tools are required for appropriate selection of traits, may provide a better understanding of crop responses to multiple stresses, especially in sodic soil. A novel strategy was developed using unmanned aerial vehicle (UAV)-thermal imaging and agglomerative hierarchical clustering-based techniques to evaluate and rank the physiological performance of 18 contrasting wheat genotypes grown on a moderately sodic and a highly sodic soil in north-eastern Australia. We obtained UAV-thermal imaging data at different times of the day (9:30, 12:00, and 15:00 hrs) close to flowering stage. Crop biophysical parameters (Leaf potassium concentration, normalized difference vegetation index, crop water uptake, stomatal conductance, plant moisture content, and aboveground biomass) were measured at close to flowering by destructive plant sampling and ground-based proximal sensing and yield was machine harvested at maturity. Canopy temperatures derived from thermal imagery between 28.9 and 35.4 °C were observed at the moderately sodic site, and between 36.2 and 41.0 °C at the highly sodic site from 9:30 to 15:00 hrs. Canopy temperature was consistently higher than corresponding ambient air temperatures indicating plant water stress at both sites. While the air temperature was not significantly different (p > 0.05) between the two sites, canopy temperature was significantly higher (p < 0.01) on highly sodic soil compared to moderately sodic soil, indicating greater water stress at the highly sodic site. This difference was most likely due to the adverse impacts of sodic soil constraints and not primarily due to environmental variations. Hence, our study revealed that sodic soil constraints can intensify plant water stress. Statistical analysis between canopy temperature (9:30, 12:00, and 15:00 hrs) and crop biophysical parameters showed close negative correlations at both moderately sodic (R2 = 0.54 to 0.83) and highly sodic (R2 = 0.30 to 0.89) sites. A closer correlation was observed at 15:00 hrs for both sites. Thus, high-resolution UAV-thermal imaging has potential to detect water-stressed plants on sodic soil. Agglomerative hierarchical clustering was used as an unsupervised machine learning tool for ranking of physiological performance of wheat genotypes. Results suggest that UAV-thermal imaging and AHC techniques can discriminate cultivars tolerant to sodicity. The study improves our understanding of crop physiological behaviour and can assist farmers in selection of water stress tolerant genotypes to sustain food security in sodic soil under water-limited environments.

Principal Component and Genetic Diversity Analysis for Seed Yield and Its Related Components in the Genotypes of Chickpea (Cicer arietinum L.)

Background: The seed yield of chickpea can be improved by selection of superior genotypes on the basis of different yield and yield component traits. These genotypes exclusively utilize in breeding programs. Yield is a complex trait which is affected by several factors, hence, a well-known technique known as principal component analysis was used to identify and minimize the number of traits for effective selection. To obtain efficient recombinants, the identified component traits need to be combined from diverse parents through recombination breeding followed by selection of transgressive segregants. Hence, the present study is envisaged to measure the genetic diversity among genotypes of chickpea.Methods: The experimental material comprised of 40 chickpea genotypes evaluated in randomized block design with three replications. The experimental unit was four rows per plot with 4 m length and spacing between row to row and plant to plant maintained as 30 x 10 cm. NPK (20:40:00) fertilizers was applied as basal doses. The data were recorded for each genotype on nine quantitative traits as per standard methods. Descriptive statistics and PCA analysis was performed by using the statistical package SPSS 16.0 version and cluster analysis was done using the Wards method of hierarchical clustering technique.Result: Out of nine PCs only three PCs exhibited more than 1.0 Eigen value and showed about 73.4% variability. PC1 contributed 28.6% of the total variation and correlated with days to flowering, days to maturity, plant height, first pod height, seeds per pod and number of pods per plant while PC2 explained 21.00% of the total variation and dominated by plant height, first pod height and seed yield. PC3 explained an additional 13.00% of the total variation and dominated by primary branches per plant. Genotype commonly found in more PC, were BG 4016, IPCB 2015-165, IPC 2011-247, GNG2459 and RKG 19-4. Hierarchical clustering technique grouped 40 genotypes into two main clusters (A and B) and nine sub clusters. The present investigation depicted that the chickpea germplasm displayed considerable genetic diversity for most of the traits under consideration.

Influence of Organic and Domestic Claims on Food Product Marketing

This article explains the relative significance of product claims in food product marketing. Eggs are selected as the appropriate product for evaluating the importance of animal welfare and product origin claims. For illustration purposes, this study uses qualitative data for the top four egg-producing countries in Europe: France, Germany, the U.K and Italy. This data is acquired from Mintel GNPD. After the hierarchical clustering technique has been applied, European egg consumers are divided into five groups: premium consumers (23.6%), product-centric consumers (19.6%), indifferent consumers (21.0%), fair consumers (12.0%), and local consumers (23.8%). Substantial differences have been found in the preferences of product claims for each cluster. The results show that both product origin and animal welfare claims are important for European egg consumers, however, each cluster has unique preferences for the intensity of these product claims. Premium, product-centric and fair consumer groups are more interested in animal welfare, while local and indifferent consumer groups consider domestic claims essential.

Diagnosing key factors for gamification in marketing using hierarchical clustering technique

Gamification is one of the most innovative and digital tools for promoting products. It has great impact on the purchase intention of the consumers. Gamification helps to retain the consumers by en...

Machine Learning-Based Gene Clustering on Brain Cancer Using K-Means and Hierarchical Clustering Methods

K-means and hierarchical clustering algorithms are employed to cluster genes according to the gene expression to determine the harming level of the genes in brain cancer. The gene expression data with a control group from The Cancer Genome Atlas database were used. The optimal cluster number for each clustering technique was obtained using the elbow method and dendrogram for K-means and hierarchical clustering methods respectively. We identified the ideal number of clusters as three and further classified them into seven groups. We observed that the second cluster contains over half the genes in healthy people and the cluster distribution of a healthy patient and a patient who died six months after being diagnosed with brain cancer is similar. Further analysis indicated that of all the time spent by patients after being diagnosed with brain cancer, group 0 has the highest percentage in one month after the diagnosis, while group -2 has the lowest percentage. Most genes shift their clusters when Kmeans and hierarchical clustering techniques we compared with the genes from the control and disease groups. The result of the measure of dissimilarity between the genes expression patterns indicates that the K-means technique outperforms the hierarchical technique with a higher rate of change in the cluster.

Diagnosing key factors for gamification in marketing using hierarchical clustering technique

Diagnosing key factors for gamification in marketing using hierarchical clustering technique

Equity Clusters through the Lens of Realized Semicorrelations

We rely on newly-developed realized semicorrelations constructed from high-frequency returnstogether with hierarchical clustering and cross-validation techniques to identify groupsof individual stocks that share common features. Implementing the new procedures basedon intraday data for the S&P 100 constituents spanning 2019-2020, we uncover distinctchanges in the "optimal" groupings of the stocks coincident with the onset of the COVID-19 pandemic. Many of the clusters estimated with data post-January 2020 evidence cleardifferences from conventional industry type classifications. They also differ from the clustersestimated with standard realized correlations, underscoring the advantages of "lookinginside" the correlation matrix through the lens of the new realized semicorrelations.

A Comparative Study of Graph Kernels and Clustering Algorithms

Graph kernels have evolved as a promising and popular method for graph clustering over the last decade. In this work, comparative study on the five standard graph kernel techniques for graph clustering has been performed. The graph kernels, namely vertex histogram kernel, shortest path kernel, graphlet kernel, k-step random walk kernel, and Weisfeiler-Lehman kernel have been compared for graph clustering. The clustering methods considered for the kernel comparison are hierarchical, k-means, model-based, fuzzy-based, and self-organizing map clustering techniques. The comparative study of kernel methods over the clustering techniques is performed on MUTAG benchmark dataset. Clustering performance is assessed with internal validation performance parameters such as connectivity, Dunn, and the silhouette index. Finally, the comparative analysis is done to facilitate researchers for selecting the appropriate kernel method for effective graph clustering. The proposed methodology elicits k-step random walk and shortest path kernel have performed best among all graph clustering approaches.

Portfolio Diversification Using Shape-Based Clustering

1. Tristan Lim 1. is a lecturer and researcher in the Department of Banking and Finance at the Nanyang Polytechnic School of Business Management in Singapore. (tristan_lim{at}nyp.edu.sg); (tris02{at}gmail.com) 2. Chin Sin Ong 1. is an assistant vice president at DBS Bank in Singapore. (chinsinong{at}dbs.com) <!-- --> 1. To order reprints of this article, please contact David Rowe at d.rowe{at}pageantmedia.com or 646-891-2157. Portfolio diversification involves lowering the correlation between portfolio assets to achieve improved risk–return exposure. It is reasonable to infer from the classic Anscombe quartet that relying on descriptive statistics, and specifically, correlation, to achieve portfolio diversification may not derive the most optimal multiperiod portfolio risk-adjusted return because stocks in a portfolio can exhibit different price trends over time, even with the same computed pairwise correlation. This research applied a shape-based time-series clustering technique of agglomerative hierarchical clustering using dynamic time-series warping as a distance measure to aggregate stocks into like-trending clusters across time as a portfolio diversification tool. Results support the use of the shape-based clustering technique for (1) portfolio allocation and rebalancing, (2) dynamic predictive portfolio construction, and (3) individual stock selection through outlier identification. The findings will be a useful addition to the existing literature in portfolio management by providing shape-based clustering as an alternative tool for portfolio construction and security selection. TOPICS: [Security analysis and valuation][1], [portfolio construction][2], [statistical methods][3] Key Findings [1]: https://www.iijournals.com/topic/security-analysis-and-valuation-0 [2]: https://www.iijournals.com/topic/portfolio-construction [3]: https://www.iijournals.com/topic/statistical-methods

Sustainability Assessment of Smallholder Agroforestry Indigenous Farming in the Amazon: A Case Study of Ecuadorian Kichwas

In the Amazon, the Yasuní Biosphere Reserve (YBR) is considered a natural and cultural diversity hotspot. It is populated by several indigenous groups, including the Kichwa, who are characterized by their traditional systems of production, which are a means of subsistence and socio-ecological integration. The objective of this research was to evaluate the sustainability of small farmers who use a traditional agroforestry system (chakra) within the buffer, transition, and core zones of the YBR. We conducted 133 interviews with Kichwa heads of households. The socio-demographic structure and distribution were identified, and the response-inducing sustainability evaluation (RISE) methodology was used to evaluate chakra sustainability according to social, economic, and ecological dimensions, expressed using 10 indicators from 50 parameters, valued from 0 (worst case) to 100 (best case). The results are expressed in a polygon, defined by the areas: (1) good performance, (2) medium performance, and (3) poor performance. We employed the multivariate classification hierarchical cluster technique and analysis of variance (ANOVA) to identify dissimilarities between groups of chakras and the existence of statistical differences, respectively. Among the studied indigenous Kichwas, a pyramidal structure progressive type was identified, which is characteristic of young populations and the nonexistence of significant differences between the RISE indicators and chakras. The lowest-scoring indicators using the RISE guidelines were: use of materials and environmental protection, animal production, economic viability and chakra administration. We provide suggestions for decision makers who support Kichwa populations in socio-productive management with sustainability goals. We to taking actions on the indicators identified with high priority to improve the sustainability in the chakras and sociodemographic dynamics.

Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise

There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer’s disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources (Gene Expression Omnibus, GEO accession ID: GSE74486). We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages.

Association between diet and periodontitis: a cross-sectional study of 10,000 NHANES participants

Periodontitis is a major cause of tooth loss globally. Risk factors include age, smoking, and diabetes. Intake of specific nutrients has been associated with periodontitis risk but there has been little research into the influence of overall diet, potentially more relevant when formulating dietary recommendations. We aimed to investigate potential associations between diet and periodontitis using novel statistical techniques for dietary pattern analysis. Two 24-h dietary recalls and periodontal examination data from the cross-sectional US NHANES, 2009-2014 (n = 10,010), were used. Dietary patterns were extracted using treelet transformation, a data-driven hierarchical clustering and dimension reduction technique. Associations between each pattern [treelet component (TC)] and extent of periodontitis [proportion of sites with clinical attachment loss (CAL) ≥ 3mm] were estimated using robust logistic quantile regression, adjusting for age, sex, ethnicity, education level, smoking, BMI, and diabetes. Eight TCs explained 21% of the variation in diet, 1 of which (TC1) was associated with CAL extent. High TC1 scores represented a diet rich in salad, fruit, vegetables, poultry and seafood, and plain water or tea to drink. There was a substantial negative gradient in CAL extent from the lowest to the highest decile of TC1 (median proportion of sites with CAL ≥ 3mm: decile 1 = 19.1%, decile 10 = 8.1%; OR, decile 10 compared with decile 1: 0.67; 95% CI: 0.46, 0.99). Most dietary patterns identified were not associated with periodontitis extent. One pattern, however, rich in salad, fruit, and vegetables and with plain water or tea to drink, was associated with lower CAL extent. Treelet transformation may be a useful approach for calculating dietary patterns in nutrition research.

Clustering of Groundwater Wells and Spatial Variation of Groundwater Recharge in Sina Basin, India

A spatial and temporal analysis of groundwater levels, topography, and precipitation is required to properly manage the groundwater resource. The present paper explains it in two parts: (1) spatial analysis of groundwater levels and selection of suitable clustering approach for selection of representative wells and (2) spatial and temporal variations of groundwater recharge calculated by three numerical models: Chaturvedi model, Amritsar model and ERAS model. Four clustering techniques including K-Means clustering algorithm, Hierarchical clustering technique, canopy and expectation maximisation (EM) were used for the clustering of groundwater levels. Among these, the canopy technique presents more reliable results compared to the other techniques for the spatial analysis of groundwater levels and the formation of representative wells in the Sina basin. For the groundwater recharge estimation, Chaturvedi model and ERAS model values were found very close. The recharge values show consistency with the precipitation data and found that 15% of precipitation contributes to annual groundwater recharge. Spatio-temporal variation of groundwater recharge correlated with precipitation is also carried out for the selected basin. The results show a drastic decline in the groundwater recharge from the year 1990 to 2008. An empirical expression is also developed for groundwater recharge estimation in terms of groundwater level. This provides regional scale information about the basin and helps to understand the groundwater exploitation scenario for instance.

CHANGE PATTERN EXPLORATION WITH HIERARCHICAL BI-CLUSTERING ON SENTINEL-1 SAR AND NIGHTTIME LIGHT DATA

Abstract. In the last few decades, urbanization activities have promoted the emergence of megacities, megalopolis, urban clusters or large urban aggregations, but only a few studies have analyzed them using remote sensing data in both the spatial and the temporal domains. In this paper, combining SAR and multispectral sensors with different resolutions, a novel approach, improved by means of a hierarchical clustering technique, is proposed. Urban changes are mapped in the form of multiple spatio-temporal patterns, visualized by change vectors exploiting the combination of SAR and nighttime light data.

Levels of Illness Uncertainty Vary by Heart Failure Symptom Cluster Group

Background Heart failure patients experience multiple concurrent symptoms called symptom clusters. Understanding the role and characteristics of symptom clusters in heart failure (HF) patients is limited. Illness uncertainty may lead to difficulty interpreting symptoms and timely treatment. Before person-centered interventions can be developed, more information about variations in HF symptom clusters and factors associated with them are needed. Purpose The purpose of this study was to describe HF symptom clusters and determine if illness uncertainty levels differed by symptom cluster. Methods A total of 129 HF patients (mean age 76.4 (12.4) years, education 10.6 (3.2) years and time with HF 3.6 (5.9) years, 54% male, 93% Caucasian, 46% widowed/divorced, NYHA class III) completed the Heart Failure Somatic Perception Scale (HFSPS, 0-90, higher score indicates more symptoms) and Mishel's Uncertainty in Illness Scale (MUIS, 23-115, higher score indicates more uncertainty) during hospitalization. Symptom clusters were determined by hierarchical agglomerative clustering techniques in SPSS 25. After controlling for differences in age, gender and length of time with HF, ANCOVA (post hoc LSD) analyses explored uncertainty levels by symptom cluster group. Results Three clusters were found: 1. Shortness of breath cluster (SOB), n = 43 (fatigue, hard to breathe, SOB when dressing, orthopnea, DOE, PND, rest during day, wake urinate at night), 2. Edema cluster, n = 47 (swollen feet, tight shoes, tight clothes, weight gain), 3. Cardiac symptom cluster, n = 39 (palpitations, chest pain, upset stomach, decreased appetite, cough). After adjusting for age, gender and length of time with HF, there was a significant difference in uncertainty levels by symptom cluster group (p = .000). Post hoc analysis demonstrated that cardiac cluster members reported less illness uncertainty than edema cluster members (69.7 vs 75, respectively, p = .033). Conclusions Results suggest that significant differences exist in illness uncertainty levels based on the symptom experience of patients with HF. Care and management of HF symptoms should include a complete assessment of unique symptom cluster profiles.

Multivariate Analysis for Assessing Irrigation Water Quality: A Case Study of the Bahr Mouise Canal, Eastern Nile Delta

Water scarcity and suitable irrigation water management in arid regions represent tangible challenges for sustainable agriculture. The current study aimed to apply multivariate analysis and to develop a simplified water quality assessment using principal component analysis (PCA) and the agglomerative hierarchical clustering (AHC) technique to assess the water quality of the Bahr Mouise canal in El-Sharkia Governorate, Egypt. The proposed methods depended on the monitored water chemical composition (e.g., pH, water electrical conductivity (ECiw), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, and SO42−) during 2019. Based on the supervised classification of satellite images (Landsat 8 Operational Land Imager (OLI)), the distinguished land use/land cover types around the Bahr Mouise canal were agriculture, urban, and water bodies, while the dominating land use was agriculture. The water quality of the Bahr Mouise canal was classified into two classes based on the application of the irrigation water quality index (IWQI), while the water quality was classified into three classes using the PCA and AHC methods. Temporal variations in water quality were investigated, where the water qualities in winter, autumn, and spring (January, February, March, April, November, and December) were classified as class I (no restrictions) based on IWQI application, and the water salinity, sodicity, and/or alkalinity did not represent limiting factors for irrigation water quality. On the other hand, in the summer season (May, June, July, August, and October), the irrigation water was classified as class II (low restrictions); therefore, irrigation processes during summer may lead to an increase in the alkalinity hazard. The PCA classifications were compared with the IWQI results; the PCA classifications had similar assessment results during the year, except in September, while the water quality was assigned to class II using the PCA method and class I by applying the IWQI. Furthermore, the normalized difference vegetation index (NDVI) around the Bahr Mouise canal over eight months and climatic data assisted in explaining the fluctuations in water quality during 2019 as a result of changing the crop season and agriculture management. Assessments of water quality help to conserve soil, reduce degradation risk, and support decision makers in order to obtain sustainable agriculture, especially under water irrigation scarcity and the limited agricultural land in such an arid region.