Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models

Najafzadeh, Mohammad; Homaei, Farshad; Farhadi, Hadi

doi:10.1007/s10462-021-10007-1

Cited by 74 publications

(32 citation statements)

References 60 publications

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“…Additionally, model efficiency in the calibration and validation periods was evaluated using the Kling-Gupta efficiency criteria (KGE and KGE') [84], the root mean square error (RMSE) [71], the Nash-Sutcliffe efficiency criterion (NSE) [85], the index of agreement (IOA) [86], the mean absolute error (MAE) [86], the mean absolute percentage error (MAPE) [87], the scatter index (SI) [88] and BIAS [86,89].…”

Section: Model Efficiencymentioning

confidence: 99%

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

Flores

Rodrı́guez

Yépez

et al. 2021

Water

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We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.

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Section: Model Efficiencymentioning

confidence: 99%

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

Flores

Rodrı́guez

Yépez

et al. 2021

Water

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“…The proposed model provides a set of Pareto-optimal solutions (WQMS locations) with trade-offs between VOI (highest information) and TE (lowest overlap). Moreover, [16] takes advantage of the multiple-kernel support vector regression algorithm for estimation of water quality parameters, and [17] used a reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams through remote sensing and data-driven models as a new approach based on artificial intelligence to improve water quality management.…”

Section: Introductionmentioning

confidence: 99%

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

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The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

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“…Artificial Intelligence (AI) plays a significant role in almost every aspect of human life, in every type of industry. For example, researchers [ 3 , 4 ] used a support vector regression algorithm to predict the water parameters. Considering physical and operational factors, another group of researchers [ 5 ] engaged AI to assess pipe break rate and [ 6 ] decoding clinical biomarker space of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Chy

Masum

Sayeed

et al. 2021

Sensors

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The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

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Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models

Cited by 74 publications

References 60 publications

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

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