| Records |
| Author |
Külls, C.; Salameh, E.; Udluft, P. |
Title  |
Assessing water supplies for irrigation-availability of natural resources and sustainability indices |
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Conference Article |
| Year |
2000 |
Publication |
Tropentag Hoffenheim |
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THL @ christoph.kuells @ Kuells2000assessing |
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63 |
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Uddin, M.G.; Diganta, M.T.M.; Sajib, A.M.; Hasan, M.A.; Moniruzzaman, M.; Rahman, A.; Olbert, A.I. |
| Title |
Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches |
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Journal Article |
| Year |
2023 |
Publication |
Heliyon |
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| Volume |
9 |
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9 |
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19668 |
| Keywords |
CCME index, Groundwater quality, Hydrogeochemistry, Irrigation indices, Nuclear power plant, Water quality index |
| Abstract |
Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3−), chloride (Cl−), phosphate (PO43−), sulfate (SO42−), nitrite (NO2−), nitrate (NO3−), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley’s ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the “poor” and “marginal” categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP. |
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2405-8440 |
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THL @ christoph.kuells @ uddin_assessment_2023 |
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167 |
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Kumar, V.; Setia, R.; Pandita, S.; Singh, S.; Mitran, T. |
| Title |
Assessment of U and As in groundwater of India: A meta-analysis |
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Journal Article |
| Year |
2022 |
Publication |
Chemosphere |
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303 |
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135199 |
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Arsenic, Geology, Groundwater, Health risk, Soil texture, Uranium |
| Abstract |
More than 2.5 billion people depend upon groundwater worldwide for drinking, and giving quality water has become one of the great apprehensions of human culture. The contamination of Uranium (U) and Arsenic (As) in the groundwater of India is gaining global attention. The current review provides state-of-the-art groundwater contamination with U and As in different zones of India based on geology and soil texture. The average concentration of U in different zones of India was in the order: West Zone (41.07 μg/L) \textgreater North Zone (37.7 μg/L) \textgreater South Zone (13.5 μg/L)\textgreater Central Zone (7.4 μg/L) \textgreater East Zone (5.7 μg/L) \textgreaterSoutheast Zone (2.4 μg/L). The average concentration of As in groundwater of India is in the order: South Zone (369.7 μg/L)\textgreaterCentral Zone (260.4 μg/L)\textgreaterNorth Zone (67.7 μg/L)\textgreaterEast Zone (60.3 μg/L)\textgreaterNorth-east zone (9.78 μg/L)\textgreaterWest zone (4.14 μg/L). The highest concentration of U and As were found in quaternary sediments, but U in clay skeletal and As in loamy skeletal. Results of health risk assessment showed that the average health quotient of U in groundwater for children and adults was less than unity. In contrast, it was greater than unity for As posing a harmful impact on human health. This review provides the baseline data regarding the U and As contamination status in groundwater of India, and appropriate, effective control measures need to be taken to control this problem. |
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0045-6535 |
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THL @ christoph.kuells @ kumar_assessment_2022 |
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161 |
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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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Journal Article |
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2024 |
Publication |
Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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168 |
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Pavelic, P.; Srisuk, K.; Saraphirom, P.; Nadee, S.; Pholkern, K.; Chusanathas, S.; Munyou, S.; Tangsutthinon, T.; Intarasut, T.; Smakhtin, V. |
| Title |
Balancing-out floods and droughts: Opportunities to utilize floodwater harvesting and groundwater storage for agricultural development in Thailand |
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Journal Article |
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2012 |
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Journal of Hydrology |
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470-471 |
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55-64 |
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Water scarcity, Flooding, Drought, Managed aquifer recharge, Floodwater harvesting, Chao Phraya River Basin |
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Summary Thailand’s naturally high seasonal endowment of water resources brings with it the regularly experienced problems associated with floods during the wet season and droughts during the dry season. Downstream-focused engineering solutions that address flooding are vital, but do not necessarily capture the potential for basin-scale improvements to water security, food production and livelihood enhancement. Managed aquifer recharge, typically applied to annual harvesting of wet season flows in dry climates, can also be applied to capture, store and recover episodic extreme flood events in humid environments. In the Chao Phraya River Basin it is estimated that surplus flows recorded downstream above a critical threshold could be harvested and recharged within the shallow alluvial aquifers in a distributed manner upstream of flood prone areas without significantly impacting existing large-medium storages or the Gulf and deltaic ecosystems. Capturing peak flows approximately 1year in four by dedicating around 200km2 of land to groundwater recharge would reduce the magnitude of flooding and socio-economic impacts and generate around USD 250M/year in export earnings for smallholder rainfed farmers through dry season cash cropping without unduly compromising the demands of existing water users. It is proposed that farmers in upstream riparian zones be co-opted as flood harvesters and thus contribute to improved floodwater management through simple water management technologies that enable agricultural lands to be put to higher productive use. Local-scale site suitability and technical performance assessments along with revised governance structures would be required. It is expected that such an approach would also be applicable to other coastal-discharging basins in Thailand and potentially throughout the Asia region. |
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0022-1694 |
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THL @ christoph.kuells @ Pavelic201255 |
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246 |
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