Comparative Study of Back-Propagation Artificial Neural Network Models for Predicting Salinity Parameters Based on Spectroscopy Under Different Surface Conditions of Soda Saline–Alkali Soils DOI Creative Commons
Yigang Jing,

Xuelin You,

Mingxuan Lu

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2407 - 2407

Published: Oct. 17, 2024

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As typical region afflicted by salinization, the soda saline–alkali soils in Songnen Plain of China demonstrate clear cracking phenomena. Nevertheless, overall spectral response to cracked surface has scarcely been studied. This study intends impact salt parameters process enhance measurement method used for salt-affected soil. To accomplish this goal, controlled desiccation experiment was carried out saline samples. A gray-level co-occurrence matrix (GLCM) calculated contrast (CON) texture feature measure extent dried Additionally, spectroscopy measurements were conducted under different conditions. Principal component analysis (PCA) subsequently performed downscale data band integration. Subsequently, prediction accuracy back-propagation artificial neural network (BP-ANN) models developed from principal components reflectance compared parameters. The results reveal that content is dominant factor determining soils, samples had highest model rather than uncracked blocks 2 mm comparison Furthermore, BP-ANN combining CON further developed, which can significantly with R2 values 0.93, 0.91, 0.74 ratio deviation (RPD) 3.68, 3.26, 1.72 salinity, electrical conductivity (EC), pH, respectively. These findings provide valuable insights into mechanism thereby advancing field hyperspectral remote sensing monitoring salinization. also aids enhancing design helpful local remediation supporting data.

Language: Английский

Suitability and its Physicochemical Characterization for Deciphering Surface Water Quality Using Entropy (E) and Fuzzy (F)-AHP Optimization Model in Mahanadi River Basin (MRB), Odisha (India) DOI
Abhijeet Das

Water science and technology library, Journal Year: 2025, Volume and Issue: unknown, P. 457 - 497

Published: Jan. 1, 2025

Language: Английский

Citations

2
Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis DOI
Mahmood Fooladi, Mohammad Reza Nikoo, Rasoul Mirghafari

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121259 - 121259

Published: June 1, 2024

Language: Английский

Citations

11
AI-driven modelling approaches for predicting oxygen levels in aquatic environments DOI Creative Commons
Rosysmita Bikram Singh, Agnieszka I. Olbert, Avinash Samantra

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 66, P. 105940 - 105940

Published: Aug. 13, 2024

Language: Английский

Citations

10
GHPSO-ATLSTM: a novel attention-based genetic LSTM to predict water quality indicators DOI
Rosysmita Bikram Singh, Kanhu Charan Patra, Avinash Samantra

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: March 17, 2024

Language: Английский

Citations

4
Surface Water Management and Geographical Information System (GIS)-Driven Optimization of Water Quality Index (WQI): A Synergistic Evaluation in Mahanadi River Basin, Odisha, India DOI
Abhijeet Das, Daniel A. Ayejoto,

Samyah Salem Refadah

et al.

Earth Systems and Environment, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

Citations

0
Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India DOI
Abhijeet Das

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102366 - 102366

Published: April 9, 2025

Language: Английский

Citations

0
Integrated Geochemical Analysis of Groundwater Quality and Human Health Risks by Using Multivariate Statistical Methods: A Case Study of Mayurbhanj District, Odisha, India DOI Creative Commons

Tejaswini Sahoo,

Jagannath Panda,

Subrat Swain

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 9, 2024

Abstract Mayurbhanj district is predominantly inhabited by tribal communities. Among the various groups in Odisha, alone accommodates 45 distinct categories. These communities primarily rely on natural water sources such as rivers, streams, and tube wells for drinking purposes without undergoing additional purification processes. Hence, investigating factors affecting groundwater quality essential to ensure its safety mitigate health risks associated with consumption of contaminated water. In present study, 145 samples from different was analysed. The geographical coordinates sample locations measurements parameters were used Geographic Information System software, ArcGIS pro, construct spatial distribution variation maps. Five significant principal components having eigen value greater than 1 total variance 73.43. Kaiser-Meyer-Olkin (KMO) test above 0.5 which shows that data collected study area are accurate analysis. Electrical conductivity, F − , pH NO 3 varies range 42 1754 µS/cm, 0.01 1.97 mg/l, 5.5 7.9 0.1 21.2 mg/l respectively. non-carcinogenic risk assessment indicates hazard quotient (HQ) values attributed fluoride ion nitrate exposure 0.43 0.46 children 0.23 0.26 adults, 0.002 0.6 0.001 0.3 comparatively at slightly more prone comparison adults. Gibbs diagram most comes region rock-water interaction dominance plot TDS vs chloride concentration. loading biplot area, first component horizontal axes has positive coefficients carbonate, chloride, bicarbonate, alkalinity, calcium hardness, magnesium dissolved solids, electrical fluoride. correlation EC (0.98), (0.525), (0.445), sulphate hardness (0.438), alkalinity (0.524), carbonate (0.528) bicarbonate (0.535). software statistical are, Minitab, Origin SPSS. results this would be useful Government policy makers provide safe community.

Language: Английский

Citations

2
A Comparative Assessment and its Characterization of the Integrated Novel Water Pollution Index and its Statistical Approach for the Evaluation of Spatial Variations Using Factor Analysis: A Geospatial Approach in Mahanadi River, Odisha DOI Creative Commons
Abhijeet Das

MATEC Web of Conferences, Journal Year: 2024, Volume and Issue: 400, P. 02007 - 02007

Published: Jan. 1, 2024

Knowledge on water quality and its assessment, is necessary for both human health environmental benefit. To account spatial distribution, surface parameters were analysed using integrated interpolation, geographical information systems (GIS) multivariate analysis. A total of 19 locations 13 indicators analysed, a duration six years (2018-2024). The study’s main objective was to assess the seasonal regional variations in index (WQI) Mahanadi River Odisha (N) pi, (S) pi , (O) (C) (E) y -WQI, Int w -WQI Multivariate Statistical tools namely Factor Analysis (F ). However, current investigation, pH, HCO 3 - Na + K Mg 2+ within permissible limits as per WHO standards. According this study, order prevalence ion concentrations signified follows: > Ca cations Cl SO 4 2- anions. analysis indicated that about 15.79% sampled area, affected by turbidity content, which highly unsuitable consumption. remaining area (84.21%) safe category water. Classification based represents most samples falls between good quality. Three noted result excessive TDS EC. In case over 84.21% fell into categories excellent, indicating suitability activities. Using results from model, reflects out samples, 16 suitable drinking. Whereas 2 polluted 1 seriously polluted, thus promotes unsuitability. Although there are several established techniques calculating WQI, study uses consider variety concerns cohesive manner. Meanwhile, y- 84.30% excellent whereas 10% 5% poor high category. Over 42.11% poor/very poor/not suitable, w- WQI diagram. Therefore, these approaches resembles precise comprehensive method comprehend relation pollution usage. later stage, factor ) can be applied lessen subjectivity dimension characteristics. It reveals first five principal components explain almost 95.61% dataset variation. This removes aggregation problems, weighting, opacity, biases seen traditional evaluation techniques. Fa suggested turbidity, TKN, primary determinants water’s amount organic released river influenced anthropogenic activity vicinity river. addition, dense habitation next manufacturing waste transported upstream downstream sources TKN urine faeces. given distribution geogenic occurrence, findings minimize uncertain causes offer insights regimes. They will also useful policy makers helping better plan, allocate resources, manage area’s potable supply.

Language: Английский

Citations

2
Surface Water Quality Evaluation and Pollution Source Analysis at the Confluence of the Wei River and Yellow River, China DOI Open Access
Jingru Zhang,

Ziqiong Hao,

Xiaohuang Liu

et al.

Water, Journal Year: 2024, Volume and Issue: 16(14), P. 2035 - 2035

Published: July 18, 2024

Water quality is a critical aspect of environmental health, affecting ecosystems, human and economic activities. In recent years, increasing pollution from industrial, agricultural, urban sources has raised concerns about the deterioration water in surface bodies. Therefore, this study investigated spatio-temporal distribution elements, health risks water, pollutant at confluence Wei River Yellow River. Using 80 samples collected during both wet dry seasons, content 22 chemistry indicators was tested. A statistical analysis, Piper diagram, entropy index were employed to analyze indicator content, hydrochemical composition, area. Moreover, risk assessment model utilized evaluate carcinogenic non-carcinogenic associated with heavy metal elements water. Finally, correlation heatmaps principal component analysis used identify potential The results indicated that Cr(VI) NH3-N main pollutants season, while season mainly influenced by F−. type area SO4Cl-CaMg. revealed high area, being primary element contributing risks. show environment soil characteristics (soils containing F− Dalí region, soils metals Tongguan region), native geological (mineral resources terrain conditions), industrial activities (ore smelting). This identified key indicators, priority control areas, extent impact River, guiding targeted management environments.

Language: Английский

Citations

2
DRSTF: A hybrid-approach framework for reservoir water temperature forecasting considering operation response DOI
Bowen Sun, Miao Yu,

Yuanning Zhang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132081 - 132081

Published: Sept. 1, 2024

Language: Английский

Citations

2