Long short-term memory for predicting daily suspended sediment concentration

Frequent and accurate estimation of suspended sediment concentration (SSC) in surface waters and hydraulic schemes is of prime importance for proper design, operation and management of many hydraulic projects. in the present study, a long short-term memory (LSTM) was considered for predicting daily suspended sediment concentration in a river. The LSTM extends recurrent neural network with memory cells, instead of recurrent units, to store and output information, easing the learning of temporal relationships on long time scales. To build the model, daily observed time series of river discharge (Q) and SSC in the Schuylkill River in the United States were used. The results of the proposed model were evaluated and compared with the feedforward neural network and the adaptive neuro fuzzy inference system models which were trained using three different learning algorithms and widely used in the literature for prediction of daily SSC. The comparison of prediction accuracy of the models demonstrated that the LSTM model could satisfactory predict SSC time series, and adequately estimate cumulative suspended sediment load (SSL).

     

Remote sensing retrieval of suspended sediment concentration in shallow waters

The dynamics of coastal lagoons and estuarine areas is characterized by a delicate balance between biological and physical processes and the comprehension and monitoring of such processes require observations over a wide range of temporal and spatial scales. Remote sensing techniques in this context are very advantageous and potentially allow overcoming the spatial limitations of traditional in situ point observations, providing new opportunities for a better understanding of the relevant bio-geomorphological processes and for the calibration and validation of spatially-distributed hydrodynamic and transport models. Remote sensing of suspended particulate matter (SPM) concentration in shallow waters must, however, overcome the difficulties associated with i) the influence of bottom reflection, which may interfere with an accurate retrieval; ii) the necessity of accurately knowing the optical properties of the suspended matter, and iii) the importance of providing an assessment of the uncertainty associated with the estimates produced. This work presents a method to estimate SPM concentration in lagoon/estuarine waters by use of a simplified radiative transfer model. We use a calibration/validation method based on cross-validation and bootstrap techniques to provide a statistically sound determination of model parameters and an evaluation of the uncertainty induced by their inaccurate determination as well as by the uncertain knowledge of the bottom sediment reflectance. The method is applied to the Venice lagoon, using observations from a network of turbidity sensors and from several multispectral satellite sensors (LANDSAT, ASTER and ALOS AVNIR). The bootstrap and cross-validation procedures employed show that consistent estimates of SPM concentration can indeed be retrieved from satellite remote sensing, provided that sufficient in situ ancillary information for appropriate calibration is available. The quantification of the estimation uncertainty shows that retrievals obtained from remote sensing are accurate, robust and repeatable. The SPM concentration maps produced show a general coherence with known features in the Venice lagoon and, together with suitable biological information, point to the role played by benthic vegetation in the stabilization of the bottom sediment.     

Estimating suspended sediment concentrations from spectral reflectance data

Abstract

A method for estimating sediment concentrations at different depths in coastal waters from measured spectral reflectance above the sea is proposed. The water backscattering coefficient is obtained by integrating a scattering cross section derived from Mie theory over a Junge type particle-size distribution. The results for turbid water, near an arid coast, with vertical mixing, shows a maximum of particle concentration in the middle layer, while the same method applied to reflectance data for ‘blue’ water, gives a relative concentration of sediment that gradually increases with depth.     

The spectral responses of algal chlorophyll in water with varying levels of suspended sediment

The purpose of this paper is to investigate the spectral responses of algal chlorophyll and water, under natural sunlight with varying suspended sediment concentrations (SSC). Twenty levels of SSC with each of two sediment types were generated, ranging from 50 to 1000 mgl^?1, in 75101 of water containing chlorophyll-a concentrations of 718 μgl^?1 and 295 μgl^?1. Results indicate that suspended sediments do not eliminate the prominent spectral patterns of algal chlorophyll, even as SSC reached 1000 mgl^?1. Between 400 and 900 nm, the relation between reflectance and SSC satisfies the expression: d²R(λ)/dS²<0. The effects of varying SSC on the positions and magnitudes of pronounced chlorophyll features were investigated. The ratio between the NIR and red wavelengths was totally independent of SSC. Thus, our finding supports using it as an index for measuring chlorophyll in natural surface water containing suspended sediments.     

Toward universal multispectral suspended sediment algorithms

A data acquisition and analysis program has been undertaken to demonstrate the feasibility of remote multispectral techniques for monitoring suspended sediment concentrations in natural water bodies. Two hundred surface radiance measurements (400–1000 nm) were made at Lake Mead with coincident water sampling for laboratory analysis. Water volume spectral reflectance is calculated from the recorded surface radiance and volume reflectance-suspended sediment relationships investigated. Statistical analysis indicates that quantitative estimates of nonfilterable residue and nephelometric turbidity can be obtained from volume spectral reflectance data with sufficient accuracy (based on U.S. Environmental Protection Agency standards) to make the multispectral technique feasible for sediment monitoring. Algorithms exhibit sufficient universality to indicate they can be implemented in many cases with little or no ground truth for calibration.     

Adaptive neuro-fuzzy computing technique for suspended sediment estimation

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation.     

Testing of a model for the simulation of the volume reflectance of water due to suspended sediment under controlled conditions,for various sediment types

Abstract

A model that simulates the volume reflectance of water is tested in laboratory conditions with three different sediments of known grain size distribution and spectral reflectance. The model uses, as its main input variables, the grain size distribution of the suspended sediment, the density of the grains and the spectral reflectance of the wet sediment from which the suspension comes. The spectral bands of the model are completely programmable and they have been adapted to correspond to green, red and near-infrared, as in Landsat MSS bands 4, 5 and 6. The grain size distribution, the reflectance in the three spectral bands and the density of the three sediment types had been measured and these measurements were used as input variables for the model to simulate the volume reflectance of the water, in the same spectral bands, for different suspended sediment concentrations. Each sediment type was used to produce several concentration levels in a specially prepared 1101 tank and the volume reflectance of the water was measured. The measurements were compared with the simulated results of the model. Then the model was modified to simulate the volume reflectance of a stratified environment, where the concentration changes with depth, and used to explain some discrepancies in the chromaticity transformation.     

A FORTRAN program for suspended sediment dynamics and tidal flux monitoring

Research into estuarine and coastal fine sediment transport and sediment deposition is aided by a proposed routine for collection and analysis of hydrodynamic, particulate, and salinity data. Field measurements taken over a number of depth intervals and over one or more tidal cycles are used to calculate the point, depth-integrated, and net horizontal movements of water, suspended solids, and salt at a fixed sampling site.     

Combining deterministic modelling with artificial neural networks for suspended sediment estimates

Estimates of suspended sediment concentrations and transport are an important part of any marine environment assessment study because these factors have a direct impact on the life cycle and survival of marine ecosystems. This paper proposes to implement a combined methodology to tackle these estimates. The first component of the methodology comprised two numerical current and wave models, while the second component was based on the artificial intelligence technique of neural networks (ANNs) used to reproduce values of sediment concentrations observed at two sites. The ANNs were fed with modelled currents and waves and trained to produce area-specific concentration estimates. The trained ANNs were then applied to predict sediment concentrations over an independent period of observations. The use of a data set that merged together observations from both the mentioned sites provided the best ANN testing results in terms of both the normalised root mean square error (0.13) and the mean relative error (0.02).     

Error assessment of validation techniques for estimating suspended particulate matter concentration from airborne multispectral imagery

Significant errors have been reported when transporting algorithms for converting aerial imagery to suspended particulate matter concentration to data collected at different times in different geographical sites. This raises the possibility that the relationships developed are coincidental rather than causal. Determination of the portability of the relationships will allow an assessment of the degree of causality of such algorithms. This paper describes the development of algorithms for two sites in UK coastal waters and the assessment of the errors incurred when porting these algorithms to datasets collected at different seasons and from varying geographical sites. The errors incurred when using algorithms at sites having differing morphologies are considered to be too large for operational use. The high accuracy of transfer to data from the same site at a different season indicates the causality of the relationship derived and allows the recommendation of a way forward for algorithm development.     

Identification of suspended sediment in coastal waters using airborne thematic mapper data

Remotely sensed data were collected using an airborne Daedalus thematic mapper, flown at 4000 m, over Swansea Bay, northern Bristol Channel, U.K. The area is one of high tidal range and wave activity. Fifty-eight surface-water/sediment samples were collected from three vessels as concurrent ‘sea-truth’ data. Samples were analysed for suspended sediment concentration (SSC)

Regression equations are developed for SSC using data in the 450-520 nm (channel 2), 520-600 nm (channel 3), 605-625 nm (channel 4) and 630-690 nm (channel 5) spectral bands

Localized flow patterns are visible in the imagery and are identified using suspended material as the passive tracer. Such patterns cannot be identified in temporally averaged data collected from research vessels

It is concluded that airborne remote sensing can contribute to the understanding of the movement of water and sediments on continental shelves, when used in association with conventional oceanographic sampling techniques.     

Atmospheric correction of LANDSAT MSS data for a multidate suspended sediment algorithm

This paper describes the application of various processing methods lo a synoptic set of satellite-sea data obtained from the Solent area on the south coast of England. The methods include ‘darkest pixel’ correction, sun angle and radiometric corrections, chromaticity analysis, atmospheric and surface corrections and the use of the satellite sensor response curves. It is shown that the use of a simple atmospheric/surface correction algorithm, based on atmospheric optical transmission theory, provides the most accurate method of estimating estuarine suspended sediment concentration from the satellite data above.     

Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery

A retrieval algorithm, of total suspended matter (TSM) concentration in the Yellow Sea (YS) and East China Sea (ECS) was developed using observations made in the 2003 Spring and Autumn cruises over the YS and the ECS. Analysis of the in-situ backscattering coefficients of the suspended particles (b_bp) indicates that the accuracy becomes worse when the concentration of TSM (C_TSM) is higher than 20 mg/l. The accuracy of the b_bp is improved by using a bio-optical model in which b_bp is optimized with a non-linear least-square Levenberg-Marquardt method. The remote sensing reflectance (R_rs) is obtained by means of the optimization. The optimized R_rs for waters with C_TSM higher than 20 mg/l, together with the measured R_rs for waters with C_TSM lower than 20 mg/l, are used to establish the relationships between R_rs(748), R_rs(869) and R_rs(645), which are used in the iterative method for atmospheric correction. Two atmospheric correction algorithms are switched according to the water turbidity. The shortwave infrared wavelengths (SWIR) method is used for waters with high-turbidity, and the iterative method is used otherwise. Results of the atmospheric correction were then applied to the Tassan model modified in this paper to compute the C_TSM. Comparison between the retrieval results from MODIS imagery and the in-situ measurements indicates that the algorithms described in this paper can provide a reliable estimation of the C_TSM distributions in the YS and ECS.     

Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters; the impact of variable particle scattering properties

This study investigates the use of single-band reflectance at visible wavelengths for the derivation of suspended sediment concentrations in the Irish Sea. A reasonably strong relationship was observed between irradiance reflectance at 665 nm (R₆₆₅) and mineral suspended sediment (MSS) concentrations. Variability in the Reflectance-MSS relationship was found to be the consequence of changes in the mass-specific scattering coefficient (b_MSS*) brought about by differences in particle properties such as grain size and composition. A systematic increase in the slope of the Reflectance-MSS relationship was observed with increasing b_MSS*. A reflectance model is presented that highlights the dependence of reflectance on b_MSS* and suggests that the errors in predicted MSS concentrations can be reduced from 56% to as little as 12% with prior knowledge of the scattering properties of the sediments under study. This paper highlights the need for a complete understanding of the scattering properties of particles in order to accurately estimate MSS concentrations from reflectance measurements. It is suggested that in order to obtain quantitative estimates of MSS in moderately turbid waters from space, it may be necessary to pre-determine scattering efficiencies, b_MSS*, for the area of interest.     

Prediction of river discharge and surface water quality using an integrated geographical information system approach

A methodology is developed for the prediction of river discharge and surface water quality (indexed by nitrogen loading) of a predominantly rural catchment using simple models in an integrated Geographical Information System (GIS). River discharge is predicted using the Soil Conservation Service (SCS) runoff Curve Number model, and surface water quality by the export coefficient model. Main input variable to these models is information on land-use along with ancillary information such as soils. Land-use is an important parameter that affects both discharge and water quality, and it can be derived from classification of remotely sensed images. Unlike conventional models, the models employed here do not require large amounts of data on several hydro-meteorological variables. The models are applied to a rural catchment in eastern England where major land-use changes have occurred in the recent past. Historical land-use data are derived from a variety of sources including maps, aerial photographs and remotely sensed satellite images for various dates ranging from 1931 to 1989. A GIS is a valuable means to enable large amounts of spatial data to be integrated, and to facilitate data manipulation for the specific application of the models. Results are validated using observed runoff and water quality records, and it is shown that the model predictions are of acceptable accuracy. This study demonstrated an application of a GIS to employ simple models to predict river discharge and water quality.     

Towards a practical remote-sensing model of suspended sediment concentrations in turbid waters using MERIS measurements

A new empirical index, termed the normalized suspended sediment index (NSSI), is proposed to predict total suspended sediment (TSS) concentrations in inland turbid waters using Medium Resolution Imaging Spectrometer (MERIS) full-resolution (FR) 300 m data. The algorithm is based on the normalized difference between two MERIS spectral bands, 560 and 760 nm. NSSI shows its potential in application to our study region – Poyang Lake – the largest freshwater lake in China. An exponential function (R² = 0.90, p < 0.01) accurately explained the variance in the in situ data and showed better performance for the TSS range 10–524 mg l^?1. The algorithm was then validated with TSS estimates using an atmospheric-corrected MERIS FR image. The validation showed that the NSSI algorithm was a more robust TSS algorithm than the band-ratio algorithms. Findings of this research imply that NSSI can be successfully used on MERIS images to obtain TSS in Poyang Lake. This work provided a practical remote-sensing approach to estimate TSS in the optically and hydrologically complex Poyang Lake and the method can be easily extended to other similar waters.     

Estimating suspended sediment concentrations in turbid coastal waters of the Santa Barbara Channel with SeaWiFS

A technique is presented for estimating suspended sediment concentrations of turbid coastal waters with remotely sensed multi-spectral data. The method improves upon many standard techniques, since it incorporates analyses of multiple wavelength bands (four for Sea-viewing Wide Field of view Sensor (SeaWiFS)) and a nonlinear calibration, which produce highly accurate results (expected errors are approximately ±10%). Further, potential errors produced by erroneous atmospheric calibration in excessively turbid waters and influences of dissolved organic materials, chlorophyll pigments and atmospheric aerosols are limited by a dark pixel subtraction and removal of the violet to blue wavelength bands. Results are presented for the Santa Barbara Channel, California where suspended sediment concentrations ranged from 0–200+ mg?l^?1 (±20?mg?l^?1) immediately after large river runoff events. The largest plumes were observed 10–30?km off the coast and occurred immediately following large El Niño winter floods.     

Prediction of concentration for microalgae using image analysis

Multimedia Tools and Applications - Maintaining the optimum growth rate and estimating the concentration of microalgae are critical in improving microalgae production. An efficient concentration...