A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India.

Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes.

Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling.

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2.

Inferences on metal pollution in the natural spawning zone of Bangladesh river and pollution management strategies.

The purpose of this study was to evaluate the metal concentrations in the Halda River in Bangladesh to determine the quality of the water and sediment in the natural spawning zone. Fe > Zn > Cr > Cd > Cu was the order of the metals in water, whereas Fe > Zn > Cd > Cu was the order in sediments. Almost all of the heavy metals in the water and sediment had been found within the established limits, with the exception of Cr and Fe in the river and Cu in the sediment.

A review on global status of fresh and saline groundwater discharge into the ocean.

Submarine groundwater discharge (SGD) is the groundwater flow from land to the sea across the seabed, and it includes both terrane freshwater and recirculated seawater in the sub-surface. This review (i) systematically evaluates findings of various quantification methodologies, (ii) examines the estimated SGD in scientific publications between 2000 and 2020, and (iii) quantitatively evaluates current situation of coastal zone management through the bibliometric analysis of research papers. Apart from enhancing the shortage of groundwater resources in coastal area, the SGD brings nutrients (nitrate and phosphate), toxic heavy metals, and organic compounds, and thus contaminate the seawater.

Seasonal behavior and accumulation of some toxic metals in commercial fishes from Kirtankhola tidal river of Bangladesh - A health risk taxation.

Contamination of fish with heavy metals (Heavy metals) is one of the most severe environmental and human health issues. However, the contamination levels in tropical fishes from Bangladesh are still unknown. To this end, the evaluated concentrations of arsenic (As), chromium (Cr), cadmium (Cd), and lead (Pb) in 12 different commercially important fish species (Tenualosa ilisha, Gudusia chapra, Otolithoides pama, Setipinna phasa, Glossogobius giuris, Pseudeutropius atherinoides, Polynemus paradiseus, Sillaginopsis panijus, Corica soborna, Amblypharyngodon mola, Trichogaster fasciata, and Wallago attu) were collected from the Kirtankhola River assess human health risk for the consumers, both in the summer and winter seasons.

Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination.

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data.

Site selection of check dams using geospatial techniques in Debre Berhan region, Ethiopia - water management perspective.

Remote sensing and GIS technology were very helpful to determine an appropriate location of freshwater storage in Amhara, Ethiopia. The techniques were used to investigate the impact of lithology, surface geomorphology, slope parameters, drainage flow, drainage density, lineament density, land cover parameters on relief, and aerial and linear features and to understand their interrelationships. Morphometric parameters such as mean stream length (Lsm), stream length ratio (RL), bifurcation ratio (Rb), mean bifurcation ratio (Rbm), relief ratio (Rh), drainage density (Dd), stream frequency (Fs), drainage texture (Rt), form factor (Rf), circularity ratio (Rc), and elongation ratio (Re) were calculated.

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination.

ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area.

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities.

Groundwater pollution index (GPI) and GIS-based appraisal of groundwater quality for drinking and irrigation in coastal aquifers of Tiruchendur, South India.

We assessed groundwater pollution index (GPI) and groundwater quality of coastal aquifers from Tiruchendur in South India for drinking and irrigation by evaluating the physico-chemical parameters of 35 samples of mainly Na-Cl type in an area of 470 km with respect to the World Health Organization (WHO) standard as well as by estimating different indices such as total hardness (TH), sodium percentage (Na%), magnesium ratio (MR), Kelley's ratio index (KR), potential salinity (PS), Langelier saturation index (LSI), residual sodium carbonate (RSC), sodium adsorption rate (SAR), permeability index (PI), and the irrigation water quality index (IWQI). Minimal influence of aquifer lithology and the dominant influence of evaporation on groundwater chemistry reflected the semi-arid climate of the study area. Electrical conductivity (EC) of about 89% of the samples across 418 km exceeded the permissible limit and Ca values of 74% of samples, however, remained within the allowable limit for drinking.

Processes and characteristics of hydrogeochemical variations between unconfined and confined aquifer systems: a case study of the Nakdong River Basin in Busan City, Korea.

This study is to assess the hydrogeochemical characteristics of groundwater at the deltaic region of the Nakdong River Basin in the Busan Metropolitan City of Korea. The study area is covered by the Quaternary sedimentary deposits and the Cretaceous granites associated with unconformity. The thick sedimentary deposits consists of two aquifers, i.

Factors controlling groundwater quality in the Yeonjegu District of Busan City, Korea, using the hydrogeochemical processes and fuzzy GIS.

The hydrogeochemical processes and fuzzy GIS techniques were used to evaluate the groundwater quality in the Yeonjegu district of Busan Metropolitan City, Korea. The highest concentrations of major ions were mainly related to the local geology. The seawater intrusion into the river water and municipal contaminants were secondary contamination sources of groundwater in the study area.

Microbial copper reduction method to scavenge anthropogenic radioiodine.

Unexpected reactor accidents and radioisotope production and consumption have led to a continuous increase in the global-scale contamination of radionuclides. In particular, anthropogenic radioiodine has become critical due to its highly volatile mobilization and recycling in global environments, resulting in widespread, negative impact on nature. We report a novel biostimulant method to effectively scavenge radioiodine that exhibits remarkable selectivity for the highly difficult-to-capture radioiodine of >500-fold over other anions, even under circumneutral pH.