Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll <i>a</i> concentration in coastal waters

Shamshirband, Shahaboddin; Nodoushan, Ehsan Jafari; Adolf, Jason E.; Manaf, Azizah Abdul; Mosavi, Amir; Chau, Kwok-wing

doi:10.1080/19942060.2018.1553742

Cited by 136 publications

(82 citation statements)

References 25 publications

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“…In the absence of streamflow discharge rate and a1, a3 data for the calculation of hydraulic turnover time in the total collection points, only data related with Group V was removed from a study of Pacheco and Van der Weijden [37]. Some studies show the importance of applying several methods that evaluate and predict groundwater levels using artificial intelligence by neural network simulation [64][65][66][67] and dendrochronology [68]. On the other hand, another study developed by Alizadeh et al [69] shows that it would be important to explore the river-flow-induced impacts in the performance of machine learning models applied for water quality-radiological parameters.…”

Section: Groundwater Sampling and Hydraulic Turnover Time Calculationmentioning

confidence: 99%

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

Martins

Pereira

Oliveira

et al. 2019

Water

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There is currently some controversy in the scientific community regarding the efficiency of the water–rock interaction process in the contamination of radon in groundwater. In this study, some difficulties were found in the sampling phase. Many of the water collection points are used for human consumption. As such, some municipalities did not want to collaborate. When this natural contaminant is undetectable to the human sense and may cause pulmonary neoplasms in the long term, it is difficult to obtain collaboration from the municipalities concerned. To overcome this controversy, it is important to understand that geogenic, climatic, hydrological, and topographic features may contribute to the effective transfer of radon from rocks to groundwater. In brief, this new approach combines the radon transfer from the geological substrate to the groundwater circulation through hierarchic agglomerative clustering (HAC) and partial least squares-path modeling (PLS-PM) methods. The results show that some lithologies with higher radon production may not always contribute to noticeable groundwater contamination. In this group, the high-fracturing density confirms the recharge efficiency, and the physical-chemical properties of the hydraulic environment (electric conductivity) plays the main role of radon unavailability in the water intended for human consumption. Besides, the hydraulic turnover time of the springs can be considered an excellent radiological indicator in groundwater. In the absence of an anomalous radioactive source near the surface, it means that the high-turnover time of the springs leads to a low-radon concentration in the water. Besides linking high-risk areas with a short period required to free local flow discharges, this study exposes the virtues of a new perspective of a groundwater contamination risk modeling.

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Section: Groundwater Sampling and Hydraulic Turnover Time Calculationmentioning

confidence: 99%

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

Martins

Pereira

Oliveira

et al. 2019

Water

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“…Some proposals examine low-cost monitoring of turbidity in drinking water [15][16][17][18]. Some work has been undertaken on low-cost sensor designs [19], distributed real-time turbidity monitoring [20,21], and using machine learning techniques for predicting sediment load in waterways [22][23][24][25][26][27]. However, few designs specifically target the application of cost-effective remote environmental monitoring, nor are commercial turbidity sensors practical for remote in situ deployment over an extended period.…”

Section: Introductionmentioning

confidence: 99%

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Trevathan

Read

Schmidtke³

2020

Sensors

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Turbidity is a key environmental parameter that is used in the determination of water quality. The turbidity of a water body gives an indication of how much suspended sediment is present, which directly impacts the clarity of the water (i.e., whether it is cloudy or clear). Various commercial nephelometric and optical approaches and products exist for electronically measuring turbidity. However, most of these approaches are unsuitable or not viable for collecting data remotely. This paper investigates ways for incorporating a turbidity sensor into an existing remote aquatic environmental monitoring platform that delivers data in near real-time (i.e., 15-min intervals). First, we examine whether an off-the-shelf turbidity sensor can be modified to provide remote and accurate turbidity measurements. Next, we present an inexpensive design for a practical light attenuation turbidity sensor. We outline the sensor’s design rationale and how various technical and physical constraints were overcome. The turbidity sensor is calibrated against a commercial turbidimeter using a Formazin standard. Results indicate that the sensor readings are indicative of actual changes in turbidity, and a calibration curve for the sensor could be attained. The turbidity sensor was trialled in different types of water bodies over nine months to determine the system’s robustness and responsiveness to the environment.

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“…In recent years, new technology of artificial intelligence (AI) and machine learning tools have begun to be used for water quality forecasts [60][61][62][63][64]. These tools are very robust; however, for obtaining good results, it is very important to accumulate local information for a water quality database.…”

Section: Resultsmentioning

confidence: 99%

Impact of Human Activities and Natural Processes on the Seasonal Variability of River Water Quality in Two Watersheds in Lampung, Indonesia

Yustika

Somura

Yuwono

et al. 2019

Water

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This study identified seasonal water quality characteristics in two adjacent mountainous rivers (Sangharus and Sekampung Hulu Rivers) in Lampung, Indonesia and determined the impacts of fertilizer application on river chemistry as a result of social forestry management. In 2016, we measured water chemistry and conducted a farmers’ questionnaire survey to obtain information on fertilizer application. The water quality results indicated that several parameters, including nitrate (NO3) and phosphate (PO4), were significantly higher in the Sangharus River than in the Sekampung Hulu River. In addition, several parameters were influenced by dilution from high river flow in the rainy season. Some parameters were likely influenced by the weathering of parent materials. By contrast, electrical conductivity (EC) and NO3 were higher in the rainy season, which was likely linked to the dominant timing of urea fertilizer application during this season. Despite the application of fertilizers in the watersheds, NO3 levels remained below the recommended standard. However, aluminum and iron concentrations were higher than the recommended level for drinking water, which was likely due to elevated soil erosion from improper land management. Therefore, we recommend that effective land management policies be implemented through the adoption of soil conservation practices for nutrient loss prevention.

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Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Abstract: wing Chau (2019) Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters, Engineering

Cited by 136 publications

References 25 publications

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Impact of Human Activities and Natural Processes on the Seasonal Variability of River Water Quality in Two Watersheds in Lampung, Indonesia

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