Ideotype based genotype selection in a multivariate dataset of sweet potato (Ipomoea batatas L.).

The dataset extensively examines the factors considered when choosing sweet potato genotypes, considering various characteristics. Notably, Moz1.15 demonstrated the highest marketable root yield at 46.46 t/ha, H5.ej.10 exhibited the highest beta-carotene level at 48.94 mg/100 g, and Moz1.9 recorded the highest vitamin C content at 23.89 mg/100 g. Moreover, there were significant correlations (ranging from 0.21 to 0.84) among the yield and quality traits studied in sweet potatoes. Principal component analysis (PCA) confirmed the connections among these traits, identifying four distinct clusters of genotypes, each characterized by specific significant combinations of traits. Factor analysis using the multi-trait genotype-ideotype index (MGIDI) highlighted the considerable impact of sweet potato traits across two growing seasons (2020-21 and 2021-22), facilitating the selection of genotypes with potential genetic gains ranging from 1.86 % to 75.4 %. Broad-sense heritability (h2) varied from 64.9 % to 99.8 %. The use of the MGIDI index pinpointed several promising genotypes, with BARI Mistialu-12 and H9.7.12 consistently performing well over both years. These genotypes exhibited both strengths and weaknesses.

An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System.

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor.

Geographic inequalities and determinants of anaemia among preeclamptic women: a cross-sectional sample-based study in Bangladesh.

BACKGROUND: Anaemia among preeclamptic (PE) women is a major undefined health issue in Bangladesh. This study explored the risk factors associated with anaemia and mapped the regional influences to understand the geographical inequalities. METHODS: Data from 180 respondents were prospectively collected from the Preeclampsia ward of Dhaka Medical College Hospital (DMCH), Bangladesh. Anaemia was defined as a blood haemoglobin level less than 11.0 g/dl. Preeclampsia was defined as systolic blood pressure (SBP) ≥ 140 mmHg and diastolic blood pressure (DBP) ≥ 90 mmHg with proteinuria. Factors associated with anaemia were explored using the chi-square test. Logistic regression (LR) was done to determine the level of association with the risk factors. RESULTS: Among the participants, 28.9% were identified as having early onset and 71.1% reported late onset of PE. 38.9% of the subjects were non-anaemic, whereas mild, moderate, and severe anaemia was found among 38.3%, 17.8%, and 5% of patients respectively. The following factors were identified; including age range 25-34 (OR: 0.169, p < 0.05), a lower education level (OR: 3.106, p < 0.05), service-holder mothers (OR: 0.604, p < 0.05), pregnancy interval of less than 24 months (OR: 4.646, p < 0.05), and gestational diabetes mellitus (OR: 2.702, p < 0.05). Dhaka district (IR: 1.46), Narayanganj district (IR: 1.11), and Munshiganj district (IR: 0.96) had the highest incidence rates. CONCLUSION: Determinants of anaemia must be considered with importance. In the future, periodic follow-ups of anaemia should be scheduled with a health care program and prevent maternal fatality and fetus morbidity in patients with PE.

Assessing tropical cyclone risk for improving mitigation strategies in Coastal Odisha, India.

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha.

Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making.

This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in 'built-up' areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157-250 km2, while the natural zone covers 91-410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak.

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.

Patterns of long-term care utilization during the last five years of life among Swedish older adults with and without dementia.

AIMS: The aims of this study were to compare the patterns of long-term care (LTC) use (no care, homecare, residential care) among people with and without dementia aged 70+ in Sweden during their last five years of life and its association with sociodemographic factors (age, gender, education, cohabitation status) and time with a dementia diagnosis. METHODS: This retrospective cohort study included all people who died in November 2019 aged 70 years and older (n = 6294) derived from several national registers. A multinomial logistic regression was conducted to identify which sociodemographic factors predicted the patterns of LTC use. RESULTS: Results showed that the time with a dementia diagnosis and cohabitation status were important predictors that influence the patterns of LTC use during the last five years of life. Nearly three-quarters of people living with dementia (PlwD) used residential care during the last five years of life. PlwD were more likely to reside in residential care close to death. Women who lived alone, with or without dementia, used residential care to a higher degree compared to married or cohabiting women. CONCLUSIONS: Among people without a dementia diagnosis, as well as those who were newly diagnosed, it was common to have no LTC at all, or use LTC only for a brief period close to death. During the last five years of life, PlwD and those living alone more often entered LTC early and used residential care for a longer time compared to people without dementia and people living alone, respectively.

Prevention of Alzheimer's disease through diet: An exploratory review.

Introduction: This exploratory review article describes about the genetic factors behind Alzheimer's disease (AD), their association with foods, and their relationships with cognitive impairment. It explores the dietary patterns and economic challenges in AD prevention. Methods: Scopus, PubMed and Google Scholar were searched for articles that examined the relationships between Diets, Alzheimer's Disease (AD), and Socioeconomic conditions in preventative Alzheimer's disease studies. Graphs and Network analysis data were taken from Scopus under the MeSH search method, including words, Alzheimer's, APoE4, Tau protein, APP, Amyloid precursor protein, Beta-Amyloid, Aß, Mediterranean Diet, MD, DASH diet, MIND diet, SES, Socioeconomic, Developed country, Underdeveloped country, Preventions. The network analysis was done through VOS viewer. Results: Mediterranean diet (MD) accurately lowers AD (Alzheimer's Disease) risk to 53% and 35% for people who follow it moderately. MIND scores had a statistically significant reduction in AD rate compared to those in the lowest tertial (53% and 35% reduction, respectively). Subjects with the highest adherence to the MD and DASH had a 54% and 39% lower risk of developing AD, respectively, compared to those in the lowest tertial. Omega-6, PUFA, found in nuts and fish, can play most roles in the clearance of Aß. Vitamin D inhibits induced fibrillar Aß apoptosis. However, the high cost of these diet components rise doubt about the effectiveness of AD prevention through healthy diets. Conclusion: The finding of this study revealed an association between diet and the effects of the chemical components of foods on AD biomarkers. More research is required to see if nutrition is a risk or a protective factor for Alzheimer's disease to encourage research to be translated into therapeutic practice and to clarify nutritional advice.

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh.

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region.

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

Analysis of urban streets and surface thermal characteristics using thermal imaging camera in residential streets of Gurugram City, India.

The thermal properties of the urban landscape are significantly affected by various human activities such as changing land use patterns, the construction of buildings and other impervious surfaces, and the development of transport systems. Urbanization often leads to the replacement of natural landscapes with impervious surfaces such as concrete and asphalt, which have a higher heat absorption capacity and lower emissivity. The continuous displacement of urban landscapes by impermeable surfaces therefore leads to an increase in urban temperatures, ultimately causing the development of the urban heat island (UHI) phenomenon. The study aims to analyze the thermal properties of physical elements in residential streets of Gurugram City using a thermal imaging camera to investigate the relationship between ambient air temperature and thermal behavior of surface materials. The study shows that the compact streets are 2-4 °C cooler than the open streets due to mutual shading of the buildings. Similarly, the temperature in the light-colored buildings is 1.5-4 °C lower than the dark buildings in the streets. In addition, a simple coat of paint over a plastered wall is much cooler than granite stone wall cladding. The study also showed how shading, whether by mutual shading or vegetative shading, can lower the surface temperature of urban materials. Building codes and design guidelines can therefore use such studies to make urban exteriors more pleasant by recommending lighter colors, plants, and local materials.

Association of Chronic Toxoplasma gondii Infection with Pro-Inflamatory Cytokine Interleukin (IL)-12 Responses in Type-2 Diabetes Mellitus Patients of Bangladesh.

Toxoplasma gondii is an intracellular protozoan parasite that causes toxoplasmosis in around one-third of the world population, particularly in pregnant women and immunocompromised individuals. Diabetes mellitus (DM) is one of the most severe global health challenges in the 21st century, and especially, type-2 diabetes mellitus (T2DM) accounts for 90% of the diabetes cases diagnosed globally. In Bangladesh, the rate of T2DM is rising gradually with the improvement in living standards. The aim of this study is to find out the correlation between latent toxoplasmosis and T2DM, emphasizing the pro-inflammatory cytokine immunity. For this, 100 (N = 100) patients with T2DM and 100 (N = 100) healthy controls were enrolled to determine the seroprevalence of toxoplasmosis using enzyme-linked immunosorbent assay (ELISA). In addition, ELISA was also performed to determine the level of pro-inflammatory cytokine, interleukin (IL)-12, to understand its role in the development of toxoplasmosis. In our study, 39.39% of the T2DM patients were positive with anti-T. gondii Immunoglobulin G by ELISA, whereas the rate of seropositivity in healthy controls was 39.73%. We did not find significant association between T. gondii infection and T2DM, but our data confirmed a high prevalence of chronic toxoplasmosis in Bangladeshi population. From hematology tests, it was found that the T2DM patients had significantly lower levels of total white blood cells (P = 0.0015), circulating eosinophils (P = 0.0026), and neutrophils (P = 0.0128) than the healthy controls. On the other hand, the levels of lymphocytes (P = 0.0204) and monocytes (P = 0.0067) were significantly higher in patients. Furthermore, T. gondii infected T2DM patients had significantly higher levels of IL-12 than the healthy controls (P = 0.026), suggesting a link between parasitic infection and IL-12 secretion. Further studies are to be performed to find out the exact cause of high prevalence of chronic T. gondii infection in Bangladeshi population.

Mapping and evaluating sustainable and unsustainable urban areas for ecological management towards achieving low-carbon city: an empirical study of Asir Region, Saudi Arabia.

Urbanisation can cause a variety of environmental and health issues, which has prompted experts to evaluate degraded areas and develop management strategies aimed at promoting urban sustainability and reducing carbon emissions. In low-carbon cities, sustainable urban areas have low carbon emission and prioritised carbon reduction by implementing sustainable transportation, green infrastructure, and energy-efficient buildings. On the other hand, unsustainable urban areas tend to lack these priorities and rely heavily on non-renewable energy sources and have high carbon emission. Therefore, this study aims to identify the most sustainable and unsustainable regions in the Abha-Khamis Mushayet Twin City region of Saudi Arabia in respect to urbanisation and carbon emission during the period between 1990 and 2020. To do so, we used Landsat datasets to create land use land cover (LULC) maps and then calculated carbon storage, emission, and absorption using InVest software. Additionally, the study examined micro-climatic conditions by calculating the urban heat island (UHI) effect, which allowed determining sustainable and unsustainable regions by comparing the UHI model and carbon similarity and mismatch model using coupling coordination degree model (CCDM). The study found that during the last three decades, the LULC pattern of the region underwent significant alterations, resulting in a substantial decline in carbon storage from 710,425 Mg C/hm2 in 1990 to approximately 527,012.9 Mg C/hm2 in 2020. Conversely, carbon emissions were observed to be very high in areas with high built-up density, with emission levels exceeding 20 tons per annum. Whilst the areas of excess carbon have decreased significantly, the areas of excess carbon emission have increased over time, resulting in the UHI effect due to high greenhouse gases. By comparing the UHI and carbon similarity and mismatch model, the researchers found that over 280 km2 of the study area is unsustainable and has increased since 1990. In contrast, only about 410 km2 of the study area is currently sustainable. To promote sustainability, the study recommends several strategies such as carbon capture, utilisation, and storage; green infrastructure; and the use of renewable energy to manage carbon emissions.

Analysis of peri-urban land use/land cover change and its drivers using geospatial techniques and geographically weighted regression.

The rate of transformation of natural land use land cover (LULC) to the built-up areas is very high in the peri-urban areas of Indian metropolitan cities. Delhi National Capital Region (Delhi NCR) is an inter-state planning region, located in the central part of India. The region has attracted a larger chunk of population by providing better economic opportunities during last few decades. This has resulted in large-scale transformation of the LULC pattern in the region. Thus, this study is intended to analyze and quantify the LULC change and its drivers in the peri-urban areas of Delhi NCR using Landsat datasets. Based on an extensive literature survey, several potential drivers of the LULC change have been analyzed using ordinary least squares (OLS) and geographical weighted regression (GWR) for the Delhi NCR. The results from LULC classification showed that the built-up area has increased from 1.67 to 7.12% of the total area of Delhi NCR during 1990-2018 while other LULC types have declined significantly. The OLS results showed that migration and employment in the tertiary sector are the most important drivers of built-up expansion in the study area. The standard residuals and local R2 results from GWR showed spatial heterogeneity among the coefficients of the explanatory variables throughout the study area. This study can be helpful for the urban policy makers and planners for making better master plan of Delhi NCR and other cities of developing countries.

Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms.

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

Modelling built-up land expansion probability using the integrated fuzzy logic and coupling coordination degree model.

The expansion of built-up area is the most noticeable form of urbanization-induced land use/land cover (LULC) change. In the global cities of south, the urban sprawl is increasing rapidly with even higher probabilities of future built-up expansion. These cities are witnessing unsustainable urban growth with no consideration of eco-friendly environmental condition and quality of life due to rapid expansion in built-up area. Indian cities too have been witnessing rapid urban growth and built-up expansion especially in the large metropolitan cities like Delhi. Therefore, the main objective of this study is to model the built-up expansion probabilities in Delhi National Capital Region (Delhi NCR) using remote sensing datasets and an integrated fuzzy logic and coupling coordination degree model (CCDM). For this, initially, the LULC classification was done using random forest (RF) classifier to extract the built-up area. Further, analytical hierarchy process (AHP) integrated fuzzy sets were applied using the extracted built-up area along with a set of economic, demographic, proximity parameters, topographic, and utility services. Five zones of built-up expansion probabilities were made namely very high, high, medium, low and very low. The result shows that the probability of built-up expansion in Delhi NCR is maximum under very high and high probability zones, whereas minimum expansion probabilities come in the very low probability zone for both base year i.e., 2018 and future years. Moreover, between base year and future years, the probability of built-up expansion has increased maximum (5.72%) under the very high zone while it declined by 14.06% in low probability zone. The validation of built-up probability using CCDM shows that the AHP integrated fuzzy logic-based probability model is robust while predicting built-up probability. The results of this study may provide useful insights for the urban planning department and policy makers to mitigate the adverse impacts of built-up expansion. Similar approach may be utilized in the analyzing the built-up urban expansion of other major cities of the world similar geographical conditions.

Application of advanced trend analysis techniques with clustering approach for analysing rainfall trend and identification of homogenous rainfall regions in Delhi metropolitan city.

In the era of global urbanization, the cities across the world are experiencing significant change in the climate pattern. However, analysing the trend and pattern of rainfall over the urban areas has a number of challenges such as availability of long-term data as well as the uneven distribution of rain-gauge stations. In this research, the rainfall regionalization approach has been applied along with the advanced statistical techniques for analysing the trend and pattern of rainfall in the Delhi metropolitan city. Fuzzy C-means and K-means clustering techniques have been applied for the identification of homogeneous rainfall regions while innovative trend analysis (ITA) along with the family of Mann-Kendall (MK) tests has been applied for the trend analysis of rainfall. The result shows that in all rain-gauge stations of Delhi, an increasing trend in rainfall has been recorded during 1991-2018. But the rate of increase was low as the trend slope of ITA and Sen's slope in MK tests are low, which varies between 0.03 and 0.05 and 0.01 and 0.16, respectively. Furthermore, none of the rain-gauge stations have experienced a monotonic trend in rainfall as the null hypothesis has not been rejected (p value > 0.05) for any stations. Furthermore, the study shows that ITA has a better performance than the family of MK tests. The findings of this study may be utilized for the urban flood mitigation and solving other issues related to water resources in Delhi and other cities.