Multi-linear Regression Approach Research Articles

Travel Time Estimation Modelling under Heterogeneous Traffic: A Case Study of Urban Traffic Corridor in Surat, India

Achievement of fast and reliable travel time on urban road network is one of the major objectives for a transport planner against the enormous growth in vehicle population and urban traffic in most of the metropolitan cities in India. Urban arterials or main city corridors are subjected to heavy traffic flow resulting in degradation of traffic quality in terms of vehicular delays and increase in travel time. Since the Indian roadway traffic is characterized by heterogeneity with dominance of 2Ws (Two wheelers) and 3Ws (Auto rickshaw), travel times are varying significantly. With this in background, the present paper focuses on identification of travel time attributes such as heterogeneous traffic, road side friction and corridor intersections for recurrent traffic condition and to develop an appropriate Corridor Travel Time Estimation Model using Multi-Linear Regression (MLR) approach. The model is further subjected to sensitivity analysis with reference to identified attributes to realize the impact of the identified attributes on travel time so as to suggest certain measures for improvement.

Field calibration of electrochemical NO<sub>2</sub> sensors in a citizen science context

Abstract. In many urban areas the population is exposed to elevated levels of air pollution. However, real-time air quality is usually only measured at few locations. These measurements provide a general picture of the state of the air, but they are unable to monitor local differences. New low-cost sensor technology is available for several years now, and has the potential to extend official monitoring networks significantly even though the current generation of sensors suffer from various technical issues. Citizen science experiments based on these sensors must be designed carefully to avoid generation of data which is of poor or even useless quality. This study explores the added value of the 2016 Urban AirQ campaign, which focused on measuring nitrogen dioxide (NO2) in Amsterdam, the Netherlands. Sixteen low-cost air quality sensor devices were built and distributed among volunteers living close to roads with high traffic volume for a 2-month measurement period. Each electrochemical sensor was calibrated in-field next to an air monitoring station during an 8-day period, resulting in R2 ranging from 0.3 to 0.7. When temperature and relative humidity are included in a multilinear regression approach, the NO2 accuracy is improved significantly, with R2 ranging from 0.6 to 0.9. Recalibration after the campaign is crucial, as all sensors show a significant signal drift in the 2-month measurement period. The measurement series between the calibration periods can be corrected for after the measurement period by taking a weighted average of the calibration coefficients. Validation against an independent air monitoring station shows good agreement. Using our approach, the standard deviation of a typical sensor device for NO2 measurements was found to be 7 µg m−3, provided that temperatures are below 30 ∘C. Stronger ozone titration on street sides causes an underestimation of NO2 concentrations, which 75 % of the time is less than 2.3 µg m−3. Our findings show that citizen science campaigns using low-cost sensors based on the current generations of electrochemical NO2 sensors may provide useful complementary data on local air quality in an urban setting, provided that experiments are properly set up and the data are carefully analysed.

Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets

Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).

Variability of OH(3-1) and OH(6-2) emission altitude and volume emission rate from 2003 to 2011

In this study we report on variability in emission rate and centroid emission altitude of the OH(3-1) and OH(6-2) Meinel bands in the terrestrial nightglow based on spaceborne nightglow measurements with the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument on the Envisat satellite. The SCIAMACHY observations cover the time period from August 2002 to April 2012 and the nighttime observations used in this study are performed at 10:00p.m. local solar time. Characterizing variability in OH emission altitude – particularly potential long-term variations – is important for an appropriate interpretation of ground-based OH rotational temperature measurements, because simultaneous observations of the vertical OH volume emission rate profile are usually not available for these measurements. OH emission altitude and vertically integrated emission rate time series with daily resolution for the OH(3-1) band and monthly resolution for the OH(6-2) band were analyzed using a standard multilinear regression approach allowing for seasonal variations, QBO-effects (Quasi-Biennial Oscillation), solar cycle (SC) variability and a linear long-term trend. The analysis focuses on low latitudes, where SCIAMACHY nighttime observations are available all year. The dominant sources of variability for both OH emission rate and altitude are the semi-annual and annual variations, with emission rate and altitude being highly anti-correlated. There is some evidence for a 11-year solar cycle signature in the vertically integrated emission rate and in the centroid emission altitude of both the OH(3-1) and OH(6-2) bands.

Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013–2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from >400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R>0.80) over the Oznet sites and worst (R=0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R=0.51 and R=0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

Children's awareness concerning emotion regulation strategies: Effects of attachment status

AbstractThe current study evaluated the effects of preschoolers' attachment status on their awareness concerning emotion regulation strategies. A total of 212 children between 3 and 5 years participated in this study and completed two self‐report tasks. The first was the Attachment Story Completion Task (ASCT), which assessed children's internal working models concerning parent–child attachment; the second evaluated children's ability to generate emotion regulation strategies in relation to three negative emotions (anger, sadness, and fear). Statistical analyses involved a mixed models multilinear regression approach controlling for age and gender. The results consistently revealed that the insecure avoidant group was significantly less likely than securely attached children to generate both comforting and self‐regulatory strategies. Surprisingly, the insecure ambivalent group showed no deficits across measured outcomes. When the analyses were conducted separately for each negative emotion, findings for co‐regulatory strategies for fear, and self‐regulatory strategies for anger also suggested that avoidantly attached children exhibited the lowest levels of awareness compared with children from the secure attachment group. These findings stress the importance of children's attachment status, and implicitly, the quality of the parent–child interactions for children's awareness of emotion regulation strategies related to negative emotions.

Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine

Simulation of groundwater level (GWL) fluctuations is an important task in management of groundwater resources. In this study, the effect of wavelet analysis on the training of the artificial neural network (ANN), multi linear regression (MLR) and support vector regression (SVR) approaches was investigated, and the ANN, MLR and SVR along with the wavelet-ANN (WNN), wavelet-MLR (WLR) and wavelet-SVR (WSVR) models were compared in simulating one-month-ahead of GWL. The only variable used to develop the models was the monthly GWL data recorded over a period of 11years from two wells in the Qom plain, Iran. The results showed that decomposing GWL time series into several sub-time series, extremely improved the training of the models. For both wells 1 and 2, the Meyer and Db5 wavelets produced better results compared to the other wavelets; which indicated wavelet types had similar behavior in similar case studies. The optimal number of delays was 6months, which seems to be due to natural phenomena. The best WNN model, using Meyer mother wavelet with two decomposition levels, simulated one-month-ahead with RMSE values being equal to 0.069m and 0.154m for wells 1 and 2, respectively. The RMSE values for the WLR model were 0.058m and 0.111m, and for WSVR model were 0.136m and 0.060m for wells 1 and 2, respectively.

Predicting the Cetane Number of Biodiesel Fuels from Their Fatty Acid Methyl Ester Composition

One of the important properties of fuel related to compression ignition (CI) engine applications is the cetane number. The present work aims to develop a universal model to predict the cetane number of any candidate biodiesel fuel based on its fatty acid methyl ester composition using a multi-linear regression approach. The biodiesel composition effects on the cetane number are captured through two new parameters, viz., straight-chain saturated factor (SCSF) and modified degree of unsaturation (DUm), which can be estimated directly from the measured biodiesel composition data. The proposed composition-based approach for predicting the cetane number of biodiesel is not limited to a specific data set. The predictions from the proposed correlation are compared to the measured cetane number of nine different biodiesel fuels of varied compositions, having wide variations of the cetane number in the range of 49–62. The comparison is found to be quite satisfactory, with a regression coefficient of 0.95 and an av...

Experimental Study of Glass Fibre Reinforced Polymer Surfaces Machined by Abrasive Water Jet Machining

Glass fibre reinforced polymer composites are being used increasingly for numerous applications. Conventional machining of glass fibre reinforced polymers is difficult because of excessive tool wear, delamination, improper kerf properties, etc. In this paper, an experimental study of influence of abrasive water jet machining process parameters on surface roughness of glass fibre reinforced polymer is presented. Process parameters namely stand-off distance, water pressure, traverse rate and abrasive mass flow rate are considered in the present work. The influence of process parameters on surface roughness is investigated using analysis of variance approach. It is observed that water pressure and abrasive mass flow rate are the most significant factors in influencing surface roughness. A set of optimum process parameters is determined on the basis of analysis of experimental data. The experimental results are successfully verified by comparing with the available literature. A regression model is developed using multi-linear regression approach on the basis of experimental analysis.

On the coupling between precipitation and potential evapotranspiration: contributions to decadal drought anomalies in the Southwest China

Under the exacerbation of climate change, climate extreme events, especially for drought, happened frequently and intensively across the globe with greater spatial differences. We used the Standardized Precipitation-Evapotranspiration Index computed from the routine meteorological observations at 269 sites in Southwest China (SWC) to study the drought characteristics (e.g., extent, duration and intensity) and their decadal variations during 1971–2012. It was revealed that the drought, in responses to the coupling between decadal precipitation and potential evapotranspiration (PET) anomalies, differed among regions and periods. For the entire SWC, droughts in 1970s and 2000s+ was generally stronger than in 1980s and 1990s with respect to their spatial extent, duration and intensity, especially in 2000s+. It was well-known that drought was closely related with a lack of precipitation; however, the impact of atmospheric demand of evaporation (reflected by PET here) on drought (e.g., duration and intensity) was rarely paid enough attentions. To that end, a spatial multi-linear regression approach was proposed in this study for quantifying the contributions of decadal PET and precipitation variations to drought duration and intensity. We have found that the contributions of decadal PET anomalies to drought duration and intensity could exceed those of precipitation, e.g., during 1980s and 1990s in SWC. Additionally, despite the strongest droughts in 2000s+, it was suggested that PET could exert comparable impacts on drought anomalies as precipitation. All these findings implied that PET plays a critical role in drought event, which acts to amplify drought duration and intensity. To sum up, this study stressed the need for enough attentions for PET processes in drought studies.

An Experimental Study of Influence of Process Parameters on Kerf Properties of Abrasive Water Jet Machined Carbon Fibre Reinforced Polymer

Abrasive water jet machining is an emerging technology for composite material processing. The machining of carbon fibre reinforced polymer by conventional means is difficult due to excessive tool wear, delamination and improper kerf properties. This paper presents experimental study of influence of process parameters on kerf top width of abrasive water jet machined carbon fibre reinforced polymer. The process parameters namely water pressure, stand-off distance, traverse rate, and abrasive mass flow rate are considered in the present work. Taguchi's design of experiment and analysis of variance are used to investigate the effect of process parameters on kerf top width. It is observed that stand-off distance and traverse rate are most significant parameters in influencing the kerf top width. A set of optimum process parameters is identified on the basis of analysis of the experimental data. A regression model is developed using multi-linear regression approach on the basis of experimental analysis. Results of the present work are successfully compared with the published literature.

Indirect and direct effects of soil properties on soil splash erosion rate in calcareous soils of the central Zagross, Iran: A laboratory study

Detachment of soil particles by means of raindrop impact is mainly affected by soil properties especially in the absence of vegetation cover. This study was conducted to investigate the direct and indirect effects of soil properties on splash erosion rate in calcareous soils of low vegetation cover in Lordegan region, Iran. For this purpose, Total Soil Splash (TSE), downslope (DSE) and upslope (USE) Splash erosion rate are measured under the following four conditions that comprised different values of Slope (S:%) and Rainfall Intensity (RI:mmh−1): 5–50, 5–80, 15–50, 15–80, respectively, using Multiple Splash Sets (MSS). As potential estimating parameters, Surface Shear Strength (SSS), Mean Weight Diameter (MWD), Organic Matter (OM), calcium carbonate, clay content, silt and sand fractions were estimated. The effects of soil estimators on soil splash erosion rate were quantified using a path analysis and Multi-Linear Regression (MLR) approaches applied to data collected from 105 points of soil profiles (A horizon). The results showed that the particle size distribution, SSS and MWD are sensitive to some of the parameters for estimating TSE, DSE and USE rate. MWD carried the highest direct effects while SSS the highest indirect ones (via MWD) on splash erosion. Particle size effects on soil splash weren't in general observed in higher slopes and rainfall intensities. The Particle size has in general a more direct rather than an indirect effect on splash erosion. Fine silt exerted an ambivalent role on splash erosion: A high detachability of fine silt directly increases splash erosion while its capability to form a surface crust, reduces splash erosion. Indirect effect of OM through its association with MWD and SSS is more than it's direct effects. Positive direct effect of calcium carbonate is more than its indirect negative effect via its association with MWD, probably due to the presence of calcium carbonate within the size of silt and fine sand in the studied soils, causing an increase in soil detachability.

A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast

AbstractA multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October–31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York–New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%–38.0%. A bias-correction technique reduces the average mean absolute error by 10%–15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010–14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model’s [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and bias-corrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.

Comparison of Two Different (Quasi-) Dynamic Testing Methods for the Performance Evaluation of a Linear Fresnel Process Heat Collector

Abstract A small-scale Linear Fresnel Collector (LFC) for the generation of process heat has been tested by Fraunhofer ISE;its performance was evaluated by means of two different methods. The first is a quasi-dynamic testing method performed according to the testing standard ISO 9806:2013, with modificationsin the model to accurately describe LFCs. Due to the two-dimensional Incidence Angle Modifier (IAM) of an LFC, an iterative multi-linear regression (MLR)approach has been developed to be able to comprehensively evaluate the optical performance. The second method is a dynamic testing method based on a parameter identification incorporating a multi-node/plug-flow collector model without strict restraints on mass flow and inlet temperature stability. Both methods are briefly described in their conceptual design and theirbasic requirements, revealing their similarities and differences. Each method is then applied to real measurement datafrom an LFC, assessing practicability and identification accuracy. For both methods, the mean absolutedifferencebetween identifiedIAM values and results from ray tracing fell in a range of 0.013-0.017, leading toa similar accuracy inLFC performance evaluation. Differences in optical efficiency between the two methodsare smaller,with anaverage absolutedifferencebelow 0.0098, even whenusing different measurement data and simulation models. Thus the dynamic methodrepresents a good starting point for the further development of an alternative dynamic testing and evaluation method with more flexibility than the current testing standard. This will be significantwhenevaluating large-scale concentrating collectors and collectors with direct steam generation.

Local models-based regression trees for very short-term wind speed prediction

This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem. We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers.

Multi-Linear Regression Based Prediction of Mass Transfer by Multiple Plunging Jets

The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90; whereas, multiple inclined plunging jets have jet impact angle of θ = 60. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multilinear regression approach, has successfully predicted the volumetric mass transfer coefficient (KLa) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets. Keywords—Mass transfer, multiple plunging jets, multi-linear regression.

Forecasting of solid waste quantity and composition: a multilinear regression and system dynamics approach

In the present study, a multiple linear regression model and system dynamics model have been proposed for forecasting municipal solid waste quantity and composition respectively. The system dynamics model is based on the comparison of results obtained using logistics, sigmoidal and growth kinetics functions. These models were applied for the case study of Delhi, the capital of India. From the results of the study, it was estimated that the daily solid waste generation rate, in Delhi, is likely to increase by more than 95% between 2011 and 2024. Further, it is estimated that there would also be a significant rise in paper and decrease in food, metals and others during the period. The contribution from the plastic and glass waste streams is expected to remain nearly constant. The changing waste characteristics shall be significantly influencing the choice of waste treatment and disposal technologies.

Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ∼2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug–enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC50 values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r2=0.67, rmsd=0.87, q2=0.65 and loormsd=0.90; the statistical results for the ligand-based MLR model were: r2=0.89, rmsd=0.51, q2=0.85 and loormsd=0.58; the statistical results for the ligand-based NN model were the best: r2=0.95, rmsd=0.33, q2=0.90 and loormsd=0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer’s disease (AD). The developed QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future.

Anthropogenic carbon changes in the Irminger Basin (1981–2006): Coupling δ13CDIC and DIC observations

The North Atlantic subpolar gyre is considered to be one of the strongest marine anthropogenic CO2 sinks, a consequence of extensive deep convection occurring during winter. Observations collected in this region since 1981 have shown large changes in Dissolved Inorganic Carbon (DIC) concentrations in intermediate and deep waters, which have been attributed to both anthropogenic CO2 penetration and natural variability in the ocean carbon cycle (Wanninkhof et al., 2010). In this context, we describe new δ13CDIC observations obtained in the Irminger Basin during two OVIDE cruises (2002 and 2006) which we compare to historical data (TTO-NAS 1981) in order to estimate the oceanic 13C Suess Effect over the more than twenty years that separates these surveys. The data reveal a significant decrease in δ13CDIC, of between −0.3‰ and −0.4‰ from 1981 to 2006. The anthropogenic change, extracted by using the extended Multi Linear Regression (eMLR) approach, explains 75% of this signal for oldest water mass and 90% for youngest. The reminding signal is due to the natural processes, such as remineralization and vertical mixing. The eMLR method was also applied to DIC measurements which i) reveal strong relationships between the increase of anthropogenic CO2 and the oceanic 13C Suess Effect over the whole water column during the 25-year period and ii) support the hypothesis of change in the Cant storage rate in the Irminger Basin between 1981 and 2006.

Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches

Prediction of sediment concentration in a river is very important for many water resource projects. Conventional sediment rating curves (SRC), however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. A comparison was performed between fuzzy logic (FL), SRC and multilinear regression models. It was based on a 5-year period of continuous streamflow, suspended sediment concentration and mean water temperature data of Sacremento Freeport Station operated by the United States Geological Survey. Based on the comparison of the results, it is found that the FL model gives better estimates than the other techniques.