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Artificial neural network model for extracting knowledge from the electro-Fenton process for acid mine wastewater treatment 从用于酸性矿山废水处理的电-芬顿工艺中提取知识的人工神经网络模型
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-30 DOI: 10.1002/clen.202400029
Anoop Kumar Maurya, Pasupuleti Lakshmi Narayana, Uma Maheshwera Reddy Paturi, Subba Reddy Nagireddy Gari

In this study, artificial neural networks (ANNs) were employed to analyze the complex interactions between electro-Fenton (EF) process variables (plate spacing, current intensity [CI], initial pH, aeration rate) and the Fe(II) and Mn(II) removal efficiency from wastewater. After experimenting with 69 different ANN architectures, the 4-8-8-2 architecture was identified as more efficient, achieving higher accuracy (adj. R2 of 0.93 for Fe(II) and 0.96 for Mn(II)) than the published model. The research provides valuable insights into the correlation between EF process parameters and removal efficiency, guiding the optimization of wastewater treatment processes. Sensitivity analysis revealed that CI significantly affects Mn(II) and Fe(II) removal efficiency. A user-friendly graphical interface was created based on the synaptic weights of the best model to enable practical predictions. It is designed to be accessible even to users without programing experience.

本研究采用人工神经网络(ANN)分析了电-芬顿(EF)工艺变量(板间距、电流强度 [CI]、初始 pH 值、曝气速率)与废水中铁(II)和锰(II)去除率之间复杂的相互作用。在尝试了 69 种不同的 ANN 架构后,4-8-8-2 架构被认为更有效,比已发表的模型具有更高的准确性(铁(II)的 R2 值为 0.93,锰(II)的 R2 值为 0.96)。这项研究为了解 EF 工艺参数与去除效率之间的相关性提供了宝贵的见解,为优化废水处理工艺提供了指导。敏感性分析表明,CI 对锰(II)和铁(II)的去除效率有显著影响。根据最佳模型的突触权重创建了一个用户友好型图形界面,以便进行实际预测。即使没有编程经验的用户也可以使用该界面。
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引用次数: 0
Effects of using a specially designed sludge draw-off pipe for circular secondary clarifiers to mitigate underflow short-circuiting 在圆形二级澄清池中使用专门设计的污泥引流管以减少底流短路的效果
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-30 DOI: 10.1002/clen.202300151
Emre Koken, Nurdan Buyukkamaci

Short-circuiting in secondary clarifiers is a well-known problem that can occur through up-flow or underflow routes. The underflow short-circuiting is not as visible as up-flow short-circuiting but can affect clarifier performance. The energy-dissipating inlet (EDI) is a type of inlet structure that is used in secondary clarifiers to dissipate the energy of larger influent volumes, allowing clarifiers to operate at higher treatment capacities. The underflow short-circuiting is encountered particularly in clarifiers equipped with EDIs. As influent volume increases, conventional draw-off pipes cannot handle high sludge capacities, deforming the sludge blanket and leading to lower concentration of solids being withdrawn. Retrofitting the design of draw-off pipes is an effective way to mitigate underflow short-circuiting and enhance treatment performance. In this study, a snail-shaped sludge draw-off pipe was designed and tested in two types of EDIs using computational fluid dynamics tools, showing a 20% increase in withdrawn sludge concentration and mitigating underflow short-circuiting potential. The optimal retrofit option was identified as equipping the clarifier with a snail-shaped draw-off pipe and an innovative EDI, known as multilayer EDI column, which would save almost half of the energy and operational costs of the biological processes while meeting discharge limits.

二级澄清池短路是一个众所周知的问题,可通过上流式或下流式途径发生。下流短路不像上流短路那么明显,但会影响澄清池的性能。消能进水口 (EDI) 是一种进水口结构,用于二级澄清池,以消散较大进水量的能量,从而使澄清池以更高的处理能力运行。在配备 EDI 的澄清池中,尤其会出现底流短路现象。随着进水量的增加,传统的引流管道无法处理高容量的污泥,污泥毯会变形,导致抽取的固体浓度降低。改造引流管的设计是缓解底流短路和提高处理性能的有效方法。在这项研究中,利用计算流体动力学工具设计了一种蜗牛形污泥引流管,并在两种类型的 EDI 中进行了测试,结果表明抽出的污泥浓度提高了 20%,并减轻了底流短路的可能性。最佳改造方案被确定为在澄清池中安装蜗牛形引流管和创新型 EDI(即多层 EDI 柱),这将节省生物处理过程近一半的能源和运营成本,同时还能满足排放限制要求。
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引用次数: 0
Modeling stage‐discharge and sediment‐discharge relationships in data‐scarce Himalayan River Basin Dhauliganga, Central Himalaya, using neural networks 利用神经网络模拟喜马拉雅山脉中部道里干嘎河流域数据稀缺的阶段-排泄量和泥沙-排泄量关系
IF 1.7 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-28 DOI: 10.1002/clen.202300388
Kuldeep Singh Rautela, Vivek Gupta, Juna Probha Devi, Lone Rafiya Majeed, Jagdish Chandra Kuniyal
This study focuses on the hydro‐sedimentological characterization and modeling of the Dhauliganga River in Uttarakhand, India. Field data collected from 2018–2020, including stage, velocity, and suspended sediment concentration (SSC), showed notable variations influenced by melting snow, glaciers, and precipitation. Challenges in accurately modeling rivers with a topography and sparse gauging stations were addressed using artificial neural networks (ANN). The calibrated models precisely predicted stage‐discharge and sediment‐discharge relationships, demonstrating the effectiveness of machine learning, particularly ANN‐based modeling, in such challenging terrains. The model's performance was assessed using coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). During the calibration phase, the model exhibited notable performance with R2 values of 0.96 for discharge and 0.63 for SSC, accompanied by low RMSE values of 5.29 cu m s–1 for discharge and 0.61 g for SSC. Subsequently, in the prediction phase, the model maintained its robustness, achieving R2 values of 0.97 for discharge and 0.63 for SSC, along with RMSE values of 5.67 cu m s–1 for discharge and 0.68 g for SSC. The study also found a strong agreement between water flow estimates derived from traditional methods, ANN, and actual measurements. The suspended sediment load, influenced by both water flow and SSC, varied annually, potentially modifying aquatic habitats through sediment deposition, and altering aquatic communities. These findings offer crucial insights into the hydro‐sedimentological dynamics of the studied river, providing valuable applications for sustainable water‐resource management in challenging terrains and addressing environmental concerns related to sedimentation, water quality, and aquatic ecosystem.
本研究的重点是印度北阿坎德邦道里甘加河的水文沉积特征和建模。2018-2020 年收集的实地数据,包括河段、流速和悬浮泥沙浓度(SSC),显示出受融雪、冰川和降水影响的显著变化。利用人工神经网络(ANN)解决了对地形复杂、测站稀少的河流进行精确建模的难题。校准后的模型精确预测了阶段-排泄量和泥沙-排泄量之间的关系,证明了机器学习,特别是基于人工神经网络的建模,在这种具有挑战性的地形中的有效性。模型的性能通过判定系数(R2)、均方根误差(RMSE)和均方误差(MSE)进行评估。在校准阶段,该模型表现出显著的性能,排水量的 R2 值为 0.96,SSC 为 0.63,同时 RMSE 值较低,排水量为 5.29 立方米/秒,SSC 为 0.61 克。随后,在预测阶段,该模型保持了其稳健性,排泄量的 R2 值为 0.97,SSC 的 R2 值为 0.63,排泄量的均方根误差值为 5.67 立方米/秒,SSC 的均方根误差值为 0.68 克。研究还发现,传统方法、ANN 和实际测量得出的水流估算值之间的一致性很高。受水流和 SSC 影响的悬浮泥沙负荷每年都有变化,可能会通过泥沙沉积改变水生生境,并改变水生群落。这些发现为研究河流的水文沉积动力学提供了重要见解,为挑战性地形中的可持续水资源管理以及解决与沉积、水质和水生生态系统相关的环境问题提供了宝贵应用。
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引用次数: 0
C, N, and P contributions to sediments of two Venezuelan coastal lagoons and their relationships with the adsorption of P 委内瑞拉两个沿海泻湖沉积物中的碳、氮和磷含量及其与磷吸附的关系
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-22 DOI: 10.1002/clen.202300266
Danilo López-Hernández, Leidy Morales, Karelys Umbría-Salinas, Astolfo Valero, Williams Melendez, Ana López-Contreras

In Unare and Píritu Coastal Lagoons, a study was carried out to assess the effect of C, N, and P load contributions on the capability of the sediments to immobilize phosphates. To achieve the later, the geochemical data of the sediments were coupled to the P-sorption index of Bache and Williams (IBW). In both lagoons, the sediments showed a pH > 7 because of calcareous sedimentation. The inorganic carbon values in the lagoons displayed a spatial distribution with higher concentrations toward the shores, that is defined by the carbonate lithology. On the contrary, the inner part of the lagoons was characterized by the presence of organic materials associated to the clay. The phosphorus content in the Unare Lagoon ranged from 290 to 625 mg kg−1, whereas in the Píritu Lagoon the values fluctuated between 213 and 1013 mg kg−1. The highest concentrations of phosphorus in both lagoons could be linked to sewage and runoff input from agricultural and livestock activities around the lagoon systems. The IBW displayed adsorption average values of 21.97 and 27.42 for Unare and Píritu Lagoon, respectively, corresponding to a rather low P sorption. In Unare Lagoon, the IBW showed positive correlations with C, N and Felabile but negative with P. However, in the Píritu Lagoon, despite the analogous lithology of the lagoons, a slightly positive non-significative correlation between IBW and IC was only found. Although the sediments adsorb P with a rather low capacity, they can mitigate the eutrophication process in the studied lagoons.

在乌纳雷和皮里图沿海泻湖进行了一项研究,以评估碳、氮和磷负荷对沉积物固定磷酸盐能力的影响。为此,将沉积物的地球化学数据与 Bache 和 Williams(IBW)的磷吸收指数结合起来。在两个泻湖中,由于钙质沉积,沉积物的 pH 值均为 7。泻湖中的无机碳值呈空间分布,碳酸盐岩质决定了泻湖沿岸的无机碳浓度较高。相反,泻湖内部的特点是存在与粘土相关的有机物质。乌纳雷泻湖的磷含量在 290 至 625 毫克/千克之间,而皮里图泻湖的磷含量则在 213 至 1013 毫克/千克之间波动。这两个泻湖中磷的浓度最高,可能与泻湖系统周围的农业和畜牧业活动产生的污水和径流有关。在乌纳雷泻湖和皮里图泻湖,IBW 的吸附平均值分别为 21.97 和 27.42,这表明磷的吸附率相当低。在乌纳雷泻湖,IBW 与 C、N 和 Felabile 呈正相关,但与 P 呈负相关。然而,在皮里图泻湖,尽管泻湖的岩性相似,但只发现 IBW 与 IC 之间存在轻微的非显著正相关。虽然沉积物对 P 的吸附能力很低,但它们可以缓解所研究泻湖的富营养化过程。
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引用次数: 0
Issue Information: Clean Soil Air Water. 8/2024 问题信息:清洁土壤、空气和水。8/2024
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-15 DOI: 10.1002/clen.202470081
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引用次数: 0
Exposure assessment of severe acute respiratory syndrome coronavirus 2 and norovirus genogroup I/genogroup II in aerosols generated by a municipal wastewater treatment plant 城市污水处理厂产生的气溶胶中严重急性呼吸系统综合征冠状病毒 2 和诺如病毒 I 基因组/II 基因组的暴露评估
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-15 DOI: 10.1002/clen.202300267
José Gonçalves, Andrés Felipe Franco, Priscilla Gomes da Silva, Elisa Rodriguez, Israel Diaz, Maria José González Peña, João R. Mesquita, Raul Muñoz, Pedro Garcia-Encina

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater and its potential as an airborne transmission source require extensive investigation, particularly in wastewater treatment plants (WWTPs), where few studies have been conducted. The aim of this study was to investigate the presence of SARS-CoV-2 and norovirus (NoV) RNA in wastewater and air samples collected from a municipal WWTP. In addition, the study assessed the potential risk of viral exposure among WWTP employees. In both the summer and winter campaigns of this study, SARS-CoV-2 and NoV RNA were quantified in wastewater/sludge samples other than effluent. Viral RNA was not detected in any of the air samples collected. The exposure risk assessment with the SARS-CoV-2 RNA concentrations in the influent pumping station of this study shows a lower risk than the calculation with the historical data provided by AquaVall, but both show a low-to-medium exposure risk for the WWTP workers. The sensitivity analysis shows that the result of the model is strongly influenced by the SARS-CoV-2 RNA quantification in the wastewater. This study underscores the need for extensive investigations into the presence and viability of SARS-CoV-2 in wastewater, especially as a potential airborne transmission source within WWTPs.

需要对废水中是否存在严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)及其作为空气传播源的可能性进行广泛的调查,尤其是在废水处理厂(WWTPs)中,因为这方面的研究很少。本研究旨在调查从一家市政污水处理厂采集的废水和空气样本中是否存在 SARS-CoV-2 和诺如病毒 (NoV) RNA。此外,研究还评估了污水处理厂员工接触病毒的潜在风险。在这项研究的夏季和冬季活动中,除污水外,还在废水/污泥样本中定量检测了 SARS-CoV-2 和 NoV RNA。在采集的空气样本中均未检测到病毒 RNA。与 AquaVall 提供的历史数据相比,根据本研究中进水泵站中的 SARS-CoV-2 RNA 浓度进行的暴露风险评估显示出较低的风险,但两者都显示出污水处理厂工人的暴露风险为低到中等。敏感性分析表明,模型结果受废水中 SARS-CoV-2 RNA 定量的影响很大。这项研究强调了对废水中 SARS-CoV-2 的存在和可行性进行广泛调查的必要性,尤其是作为污水处理厂内潜在的空气传播源。
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引用次数: 0
Concentrations and health risk assessment of selected cations and anions in drinking water in Antalya, Türkiye 土耳其安塔利亚饮用水中某些阳离子和阴离子的浓度及健康风险评估
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-09 DOI: 10.1002/clen.202300451
Murat Kilic

This study investigates the potential health risks associated with ion concentrations in fountains used for drinking water in the Antalya/Konyaaltı tourism center region. A total of 32 fountain samples commonly used during the summer were analyzed to determine the anion and cation concentrations using ion chromatography equipped with a conductivity detector. The results showed high accuracy and reliability, with R2 values ranging between 0.998 and 0.999 and recovery values between 97% and 110% at low and high concentrations. The method detection limit and method quantification limit were determined as 0.004 and 0.276, respectively, whereas the pH values ranged from 6.71 to 7.69. When examining the average fluoride and nitrate levels in different age groups, the nitrate daily acceptable intake (total hardness index) values were found to be 0.175, 0.091, and 0.076 µg kg−1 body weight per day for children, adolescents, and adults, respectively. Overall, the study suggests that children have the highest exposure levels due to their lower body weight, and exposure levels decrease with increasing age.

本研究调查了与安塔利亚/孔亚尔特旅游中心地区饮用水喷泉中离子浓度相关的潜在健康风险。共对 32 个夏季常用的喷泉样本进行了分析,利用配备电导检测器的离子色谱法确定阴离子和阳离子的浓度。结果表明,该方法准确可靠,R2 值在 0.998 和 0.999 之间,低浓度和高浓度的回收率在 97% 和 110% 之间。方法检测限和方法定量限分别为 0.004 和 0.276,pH 值范围为 6.71 至 7.69。在检测不同年龄组的氟化物和硝酸盐平均含量时,发现儿童、青少年和成人的硝酸盐每日可接受摄入量(总硬度指数)分别为 0.175、0.091 和 0.076 微克/千克-1 体重。总体而言,研究表明,儿童的暴露水平最高,因为他们的体重较轻,而暴露水平会随着年龄的增长而降低。
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引用次数: 0
Arsenic and zinc concentrations in wheat grains under soil zinc application and arsenic-contaminated irrigation 土壤施锌和砷污染灌溉条件下小麦籽粒中的砷和锌浓度
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-08-06 DOI: 10.1002/clen.202300106
Ammara Basit, Shahid Hussain

Above-permissible levels of arsenic (As) in irrigation water lead to toxic levels in wheat grains, increasing health risks for humans. In this study, two zinc (Zn)-biofortified wheat (Triticum aestivum L.) cultivars (Akbar-2019 and Zincol-2016) were grown in pots with two Zn application rates (0 and 8 mg Zn kg−1) and three levels of As in irrigation water (distilled-water control, 100 and 1000 µg As L−1). Irrigation with As-contaminated water decreased dry matter yields, concentrations of grain phosphorus (P) and Zn, and estimated daily intake (EDI) of Zn. Conversely, it increased grain As concentration and As EDI. Soil Zn application mitigated the negative effects of As on dry matter yields of both cultivars while simultaneously enhancing grain Zn concentration and Zn EDI. On average, Zn application increased grain Zn concentration by 114% compared to no Zn application. Additionally, Zn application decreased grain As concentration at all As levels. In conclusion, this study suggests that applying Zn to Zn-biofortified wheat irrigated with As-contaminated water can mitigate the toxic effects of As on wheat. It increase Zn concentration and decrease As concentration in wheat grains, which is vital for enhancing grain quality for human consumption.

灌溉水中砷(As)含量超过允许值会导致小麦籽粒中的砷含量达到有毒水平,从而增加人类的健康风险。在这项研究中,两个锌(Zn)生物强化小麦(Triticum aestivum L.)栽培品种(Akbar-2019 和 Zincol-2016)在两种锌施用率(0 和 8 mg Zn kg-1)和三种砷含量(蒸馏水对照、100 和 1000 µg As L-1)的灌溉水中进行了盆栽。用砷污染的水灌溉会降低干物质产量、谷物磷(P)和锌的浓度以及锌的估计日摄入量(EDI)。相反,却增加了谷物的砷浓度和砷的 EDI。土壤施锌减轻了砷对两个品种干物质产量的负面影响,同时提高了谷物的锌浓度和锌的 EDI。与不施锌相比,施锌可使谷物锌浓度平均提高 114%。此外,在所有砷浓度水平下,施锌都能降低谷物的砷浓度。总之,这项研究表明,在用砷污染的水灌溉的锌生物强化小麦中施用锌可以减轻砷对小麦的毒性影响。它能提高小麦籽粒中的锌浓度,降低砷浓度,这对提高供人类食用的谷物品质至关重要。
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引用次数: 0
Assessment of temporal and spatial variations of water quality parameters in the Zarafshan River basin 扎拉夫山河流域水质参数的时空变化评估
IF 1.5 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-07-29 DOI: 10.1002/clen.202300454
Shobegim Shoergashova, Tie Liu, Nigora Ibrokhimova, Liliya Latisheva, Bakhtiyor Karimov

River ecosystems in Central Asia face significant stress from environmental changes and pollution. This study assesses temporal and spatial variations in water quality parameters within the Zarafshan River Basin using retrospective data and field measurements. Water quality indicators, including electrical conductivity (EC), total suspended solids (TSS), ammonium nitrogen (N-NH4), nitrite nitrogen (N-NO2), nitrate nitrogen (N-NO3), temperature (T), chemical oxygen demand (COD), dissolved oxygen (DO), and discharge, were analyzed using the Pearson's correlation coefficient and ANOVA, with the Mann–Kendall (MK) test detecting trends over time. Obtained results indicate significant seasonal effects with elevated TSS during summer, increasing sediment load and changing aquatic habitats. The strong inverse correlation (–0.89) between DO and N-NH4 signifies ecological challenges particularly in low DO concentrations during summer (3.25 mg L–1). Long-term analysis identifies Navoiazot chemical factory as a major pollution hotspot. Spatial analyses based on extended sampling have revealed the Siab and Dargom canals and Samarkand City as major pollution sources of elevated N-NO2 and COD, respectively. Trends at various gauging stations (MK-test) show increasing EC (τ = 0.72) and N-NH4 (τ = 0.46) levels, with decreasing TSS, N-NO3, T, and COD levels over time. Recommendations include targeted measures to reduce pollution at the Navoiazot factory and downstream, introducing sustainable agriculture practices, increasing public awareness for environmental conservation, and improving urban wastewater treatment to meet water quality requirements for different users.

中亚地区的河流生态系统面临着环境变化和污染带来的巨大压力。本研究利用回顾性数据和实地测量,评估了扎拉夫山河流域水质参数的时空变化。采用皮尔逊相关系数和方差分析法分析了水质指标,包括电导率 (EC)、总悬浮固体 (TSS)、铵态氮 (N-NH4)、亚硝酸盐氮 (N-NO2)、硝酸盐氮 (N-NO3)、温度 (T)、化学需氧量 (COD)、溶解氧 (DO) 和排水量。结果表明,夏季总悬浮固体含量升高,增加了沉积物负荷,改变了水生生境,具有明显的季节性影响。溶解氧与 N-NH4 之间存在强烈的反相关关系(-0.89),这表明在夏季溶解氧浓度较低(3.25 毫克/升)的情况下,生态环境尤其面临挑战。长期分析表明,纳沃亚佐特化工厂是一个主要的污染热点。基于扩展采样的空间分析表明,Siab 和 Dargom 运河以及撒马尔罕市分别是 N-NO2 和 COD 升高的主要污染源。各测量站的趋势(MK 测试)显示,随着时间的推移,EC(τ = 0.72)和 N-NH4 (τ = 0.46)水平不断上升,而 TSS、N-NO3、T 和 COD 水平则不断下降。建议包括采取有针对性的措施减少 Navoiazot 工厂和下游的污染,引入可持续农业实践,提高公众的环境保护意识,改善城市污水处理以满足不同用户的水质要求。
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引用次数: 0
Performance evaluation of different machine learning algorithms for prediction of nitrate in groundwater in Thiruvannamalai District 用于预测 Thiruvannamalai 地区地下水中硝酸盐含量的不同机器学习算法的性能评估
IF 1.7 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Pub Date : 2024-07-18 DOI: 10.1002/clen.202400060
Christina Jacob, Uma Shankar Masilamani
The prevalence of nitrates (NO3) in groundwater due to the extensive application of fertilizers and anthropogenic sources pollutes the groundwater. Machine learning (ML) techniques are now being increasingly deployed to achieve high precision in predicting water quality. This study assesses the efficacy of nine distinct ML algorithms, namely, linear regression, polynomial regression, decision tree, random forest (RF), support vector machine, multilayer perceptron regressor, eXtreme gradient boosting (XGB), light gradient boosting (LGB), and K‐nearest neighbors to predict nitrate concentration in the groundwater in Thiruvannamalai District, Tamil Nadu. Overall, 360 water samples for 1 year and 14 groundwater variables were determined to predict nitrate. Performance evaluation metrics such as root mean square error (RMSE), moving average error (MAE), and correlation coefficient (R2) were evaluated for pre‐monsoon, monsoon, and post‐monsoon seasons. For all three seasons, RF predicted the nitrate concentration with low values of RMSE, MAE, and higher values of R2. The results show values for RF with: RSME: 0.49, MAE: 1.30, and R2: 0.94, which has a higher prediction tailed by LGB and XGB and is true for all the seasons. The results from the study will aid the policymakers in planning the strategy for remediation.
由于大量施用化肥和人为污染源,地下水中的硝酸盐(NO3-)普遍存在,对地下水造成了污染。目前,越来越多地采用机器学习(ML)技术来实现高精度的水质预测。本研究评估了九种不同的 ML 算法,即线性回归、多项式回归、决策树、随机森林 (RF)、支持向量机、多层感知器回归器、极梯度提升 (XGB)、轻梯度提升 (LGB) 和 K 最近邻预测泰米尔纳德邦 Thiruvannamalai 地区地下水中硝酸盐浓度的功效。总体而言,通过对 1 年内 360 个水样和 14 个地下水变量的测定来预测硝酸盐。评估了季风前、季风中和季风后季节的性能评估指标,如均方根误差 (RMSE)、移动平均误差 (MAE) 和相关系数 (R2)。在所有三个季节中,RF 预测硝酸盐浓度的 RMSE 和 MAE 值较低,R2 值较高。结果显示,RF 值为RSME:0.49,MAE:1.30,R2:0.94:0.94,其预测尾数高于 LGB 和 XGB,并且在所有季节都是如此。研究结果将有助于决策者规划补救战略。
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引用次数: 0
期刊
Clean-soil Air Water
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