Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales

Teixeira, Laís Coelho; Mariani, Priscila Pacheco; Pedrollo, Olavo Correa; Castro, Nilza Maria dos Reis; Sari, Vanessa

doi:10.1007/s11269-020-02647-9

Cited by 15 publications

(8 citation statements)

References 36 publications

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“…To analyze model performance, we compared the estimated to the observed results with coefficients that measure the average tendency of the simulated values to be larger or smaller than their observed ones: Nash-Sutcliffe (NS) coefficient [25] as suggested by Teixeira et al [13] and Moriasi et al [26], and percentage bias (PBIAS) [27] according to Moriasi et al [26]: NS > 0.8 and PBIAS < 10 -Excellent; 0.70 < NS ≤ 0.8 and 10 ≤ PBIAS < 15 -Good; 0.45 < NS ≤ 0.7 and 15 ≤ PBIAS 20 -Satisfactory; and NS ≤ 0.45 and PBIAS ≤ 20 -Poor.…”

Section: Model Performancementioning

confidence: 99%

“…These models should rely on input variables obtained at lower costs in relation to traditional collection methods. Artificial intelligence has been successfully applied in the field of water resource management [13,14], and in recent years models based on artificial neural networks (ANN) have attracted researchers to the study of water-soilenvironment interactions.…”

Section: Introductionmentioning

confidence: 99%

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Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

et al. 2022

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The nonlinear dynamics of the determining factors of the morphometric characteristics of cracks in expansive soils make their typification a challenge, especially under field conditions. To overcome this difficulty, we used artificial neural networks to estimate crack characteristics in a Vertisol under field conditions. From July 2019 to June 2020, the morphometric characteristics of soil cracks (area, depth and volume), and environmental factors (soil moisture, rainfall, potential evapotranspiration and water balance) were monitored and evaluated in six experimental plots in a tropical semiarid region. Sixty-six events were measured in each plot to calibrate and validate two sets of inputs in the multilayer neural network model. One set was comprised of environmental factors with significant correlations with the morphometric characteristics of cracks in the soil. The other included only those with a significant high and very high correlation, reducing the number of variables by 35%. The set with the significant high and very high correlations showed greater accuracy in predicting crack characteristics, implying that it is preferable to have fewer variables with a higher correlation than to have more variables of lower correlation in the model. Both sets of data showed a good performance in predicting area and depth of cracks in the soils with a clay content above 30%. The highest dispersion of modeled over predicted values for all morphometric characteristics was in soils with a sand content above 40%. The model was successful in evaluating crack characteristics from environmental factors within its limitations and may support decisions on watershed management in view of climate-change scenarios.

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Section: Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

et al. 2022

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“…Many techniques of artificial intelligence (AI), including multiple linear regression (MLR), 7 the artificial neural network (ANN), [8][9][10] and the support vector machine (SVM), [11][12][13][14] have been applied to the process of water treatment. 8,[15][16][17][18][19][20][21][22][23] Many hybrid AI technologies used to determine the quality of water have also emerged in recent years, and have been used for making observations based on remote sensing, predicting the distribution of the water plant, and decision making. 24,25 A number of models have been applied to predict the turbidity of the effluent [26][27][28][29][30][31] and the requisite coagulant dosage.…”

Section: Introductionmentioning

confidence: 99%

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Wang

et al. 2023

Environ. Sci.: Water Res. Technol.

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“…With the continuing advancement in remote sensing technology, suspended sediment monitoring methods have evolved to incorporate more mature physical models, empirical models, and semi-empirical models [2,[15][16][17]. Among these methods, analytical models, rooted in rigorous radiative transfer theory, have a great application potential.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolutions of the Suspended Particulate Matter in the Yellow River Estuary, Bohai Sea and Characterized by Gaofen Imagery

Yu,

Zhang,

Chen

et al. 2023

Remote Sensing

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Suspended particulate matter is a crucial component in estuaries and coastal oceans, and a key parameter for evaluating their water quality. The Bohai Sea, a huge marginal sea covering an expanse of 77,000 km² and constantly fed by numerous sediment-laden rivers, has maintained a high level of total suspended particulate matter (TSM). Despite the widespread development and application of TSM retrieval algorithms using commonly available satellite data like Landsat, Sentinel, and MODIS, developing TSM retrieval algorithms for China’s Gaofen (GF) series (GF-6 and GF-1) in the Bohai Sea is still a great challenge, mainly due to the limited applicability of empirical algorithms. In this study, 259 in situ measured-TSM samples were collected for algorithm development. The remote sensing reflectance (Rrs) curve demonstrates prominent peaks between 550 and 580 nm. Through conversion to remote sensing reflectance, it was found that single-band data had a weak correlation with TSM, reaching a maximum correlation of 0.44. However, by combining bands of band ratio calculations, the correlation was enhanced. Particularly, the blue and green band equivalent Rrs ratio had a correlation coefficient of 0.81 with TSM, and the proposed TSM inversion exponential algorithm developed based on this factor obtained an R-squared (R²) value of 0.76 and a mean relative error (MRE) of 32.24%. Analysis results indicated that: (1) there are spatial variations in the TSM within the Bohai Sea, Laizhou Bay, and the Yellow River estuary, with higher levels near the coast and lower levels in open waters. The Yellow River estuary experiences seasonal fluctuations higher TSM during spring and winter, and lower variations during summer and autumn, and (2) the dynamics of TSM are affected by Yellow River runoff, with increased runoff leads to higher TSM levels and expanded turbid zones. This study proposes a new algorithm to quantify TSM evolutions and distributions in the Bohai Sea and adjacent regions using China’s Gaofen imageries.

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Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales

Cited by 15 publications

References 36 publications

Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

Successful prediction for coagulant dosage and effluent turbidity of a coagulation process in a drinking water treatment plant based on the Elman neural network and random forest models

Spatiotemporal Evolutions of the Suspended Particulate Matter in the Yellow River Estuary, Bohai Sea and Characterized by Gaofen Imagery

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