Unplanned hysterectomy: a comprehensive analysis of race, ethnicity, sociodemographic factors, pregnancy complications, and cardiovascular disease risk factors.

OBJECTIVE: To understand the risk of unplanned hysterectomy (UH) in pregnant women better in association with maternal sociodemographic characteristics, cardiovascular disease (CVD) risk factors, and current pregnancy complications. DESIGN: Using Florida birth data from 2005 to 2014, we investigated the possible interactions between known risk factors of having UH, including maternal sociodemographic characteristics, maternal medical history, and other pregnancy complications. Logistic regression models were constructed. Adjusted odds ratios and 95% confidence intervals were reported. RESULTS: Several interactions were observed that significantly affected odds of UH. Compared to non-Hispanic White women, Hispanic minority women were more likely to have an UH. The overall risk of UH for women with preterm birth (<37 weeks) and concurrently had premature rupture of membranes (PRoM), uterine rupture, or a previous cesarean delivery was significantly higher than women who delivered to term and had no pregnancy complications. Women who delivered via cesarean who also had preeclampsia, PRoM, or uterine rupture had an overall increased risk of UH. Significantly decreased risk of UH was seen for Black women less than 20 years old, women of other minority races with either less than a high school degree or a college degree or greater, women of other minority races with PRoM, and women with preterm birth and diabetes compared to respective reference groups. CONCLUSIONS: Maternal race, ethnicity, CVD risk factors, and current pregnancy complications affect the risk of UH in pregnant women through complex interactions that would not be seen in unadjusted models of risk analysis.

Dynamics of particles and phosphorus in canals of the Lower Everglades, Florida, USA.

Water flow (discharge) can affect water quality by influencing the concentration and transport of waterborne contaminants. The effects of discharge on phosphorus (P) and particle concentrations in managed canals, were described using concentration-discharge (C-Q) relationships, accumulation of suspended and settling particles, and the physicochemical characteristics of these particles and bed sediments. Piecewise regression analysis on C-Q relationships revealed slope inflections that denoted thresholds, where P-behavior changed from low to high discharge. The C-Q relationships generally showed higher concentrations at higher discharges. In three of the four Lower Everglades canals studied, long-term (1995-2019) lower temporal resolution data (daily to weekly) was adequate to describe the influence of discharge on P concentrations. However, in one site, the L-29 Canal, higher temporal resolution data (minutes to hours over weeks), derived from acoustic sensors, was necessary to produce C-Q relationships. In the L-29 Canal, discharge affected the transport, settling, and sediment accrual at distances from the S333 inflow structure. Sediment traps showed higher discharge led to a greater accumulation of suspended particles that were transported and settled farther downstream. Generally, downstream surface sediments in the L-29 Canal had greater organic matter, lower bulk density and higher TP than those of the upstream site, reflecting long-term effects of discharge. Understanding the effects of discharge on particles and associated nutrients, especially at discharge thresholds that lead to concentration increases, can inform the operation of managed canals to reduce contaminant loading to downstream sensitive ecosystems.

Hydrological drought impacts on water storage variations: a focus on the role of vegetation changes in the East Africa region. A systematic review.

Drought is one of the most challenging climatic events. Recently, the drought influence in East Africa (EA) total water storage (TWS) is a serious problem, particularly in arid areas with modified natural vegetation relying on water deficit, garnered extensive research interest. Hydro-climatological and vegetation indices and remote sensing datasets derived from Gravity Recovery Climate Experiment (GRACE) mission datasets reveal good performance in analyzing hydrological drought influences in water storage. Over the last decades, studies were considered successful in monitoring the drought influence in the region TWS potential. However, several challenges remained unsolved, hindering the hydrological drought mitigation strategies. This review deals with an overview of drought impact monitoring targeted at the TWS variation with the response of vegetation change for sustainable drought mitigation. To improve the flexibility and adaptive capacities of the water deficit problem, we aim to provide an overview of drought impacts on TWS in the region to redefine the hydro-climatological and vegetation drought indices and improve the understanding of drought impact through remote sensing datasets. This review presents the challenges and prospects and offers a conclusion. Although, we hope that the review can facilitate further study regarding future hydrological drought projection in the development of several scientific research in the field.

Crop production response to soil moisture and groundwater depletion in the Nile Basin based on multi-source data.

Soil moisture (SM) and groundwater (GW) depletion triggered by anthropogenic and natural climate change are influencing food security via crop production per capita decrease in the Nile River Basin (NRB). However, to the best of our understanding, the causes and impact of SM and GW depletion have not been studied yet comprehensively in the NRB. In this study, GW is derived from the Gravity Recovery and Climate Experiment (GRACE) mission, and SM was estimated using the Triple Collocation Analysis (TCA). SM/GW depletion causes were evaluated via the Land Use Land Cover (LULC) and rainfall/temperature change analysis, whereas impact analysis focused on crop production per capita reduction (food insecurity) during SM depletion. The major findings of this study are 1) TCA analyzed SM show a decreasing trend (-0.06 mm/yr) in agricultural land while increasing (+0.21 mm/yr) in forest land, 2) LULC analysis indicated a vast increment of agricultural land (+9%) and bareland (+9%) although the decreasing pattern of forest (-1.5%) and shrubland (-6.9%) during 1990-2019; 3) the impact of SM depletion on crop production per capita caused food insecurity during a drought year, 4) agriculture drought indices and crop production per capita show high correlations (R2 = 0.86 to 0.60) demonstrated that Vegetation Supply Water Index (VSWI) could provide strategic warning of drought impacts on rainfed agricultural regions. In conclusion, SM and GW depletions are mainly caused by human-induced and climate change factors imposing food insecurity challenges in the NRB coupled with increasing temperature and excessive water extraction for irrigation. Therefore, it is highly recommended to rethink and reverse SM/GW depletion causing factors to sustain food security in NRB and similar basins.

How suitable are satellite rainfall estimates in simulating high flows and actual evapotranspiration in MelkaKunitre catchment, Upper Awash Basin, Ethiopia?

Understanding the suitability of Satellite Rainfall Estimates (SREs) in simulating high flows and Actual Evapotranspiration (AET) is crucial for developing flood monitoring systems. Therefore, this study aims to assess i) the suitability of SREs in simulating both high flows and AET for different levels of model complexity, and ii) the effect of streamflow calibration on simulating AET for different rainfall inputs in Melkakunitre catchment, Upper Awash Basin, Ethiopia. Three state-of-the-art SREs (TRMM 3B42v7, IMERG v06B, and TAMSAT v3) were used and their usefulness in simulating high flows (Q5), daily streamflow, and wet season flows (from June to September) was assessed using the HBV-light model for the period 2003-2015. The model was set up for two levels of complexity: with and without considering the effect of orography on rainfall and temperature. Moreover, the water balance derived AET was compared against three remotely sensed AET products, MOD 16A2, GLEAM v3, and SSEBob, so as to examine the effect of streamflow calibration on AET simulation. Results show that rainfall inputs and model complexity have a strong impact on simulating streamflow and AET. For all rainfall forcing datasets, the performance of the hydrological model improves when we consider the effects of orography on rainfall and temperature. The IMERG v06B and TAMSAT v3 products showed the highest and least performances in simulating all the three flow conditions, respectively. Moreover, the MODIS-AET is the best remotely sensed AET product in reproducing the water balance-derived AET for all rainfall inputs except TAMSAT v3. The HBV-light model parameters calibrated with streamflow provided better results for simulating AET as well. On average, the usefulness of the IMERG v06B product for simulating high flows and AET is outstanding and can be thus used for developing flood monitoring and management systems in the study catchment.

Long-Term Assessment of Surface Water Quality in a Highly Managed Estuary Basin.

Anthropogenic developments in coastal watersheds cause significant ecological changes to estuaries. Since estuaries respond to inputs on relatively long time scales, robust analyses of long-term data should be employed to account for seasonality, internal cycling, and climatological cycles. This study characterizes the water quality of a highly managed coastal basin, the St. Lucie Estuary Basin, FL, USA, from 1999 to 2019 to detect spatiotemporal differences in the estuary's water quality and its tributaries. The estuary is artificially connected to Lake Okeechobee, so it receives fresh water from an external basin. Monthly water samples collected from November 1999 to October 2019 were assessed using principal component analysis, correlation analysis, and the Seasonal Kendall trend test. Nitrogen, phosphorus, color, total suspended solids, and turbidity concentrations varied seasonally and spatially. Inflows from Lake Okeechobee were characterized by high turbidity, while higher phosphorus concentrations characterized inflows from tributaries within the basin. Differences among tributaries within the basin may be attributed to flow regimes (e.g., significant releases vs. steady flow) and land use (e.g., pasture vs. row crops). Decreasing trends for orthophosphate, total phosphorus, and color and increasing trends for dissolved oxygen were found over the long term. Decreases in nutrient concentrations over time could be due to local mitigation efforts. Understanding the differences in water quality between the tributaries of the St. Lucie Estuary is essential for the overall water quality management of the estuary.

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration.

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights ( h r m s ), and (iii) SAR VV, h r m s , and optimal surface correlation length ( l e f f ). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( h r m s and l e f f ) were incorporated into the prediction model. Consequently, the ANN's prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes.

The severity and frequency of climate extremes will change in the future owing to global warming. This can severely impact the natural environment. Therefore, it is common practice to project climate extremes with a global climate model (GCM) in order to quantify and manage the associated risks. Several studies have demonstrated that a multi-model ensemble approach increases the reliability of predictions by exploiting the strengths and discounting the weaknesses of each climate simulator. However, the available multi-model averaging approaches exhibit significant drawbacks as they are not capable of extracting different climate extreme characteristics from the climate models. This study proposes a new approach that combines multiple models for projecting climate extremes by accounting for different extreme indices in the climate model performance weighting scheme. The capability of this method was evaluated with respect to reliability ensemble averaging (REA) and Taylor diagram-based GCM skill approaches for reproducing wet and dry precipitation events. The proposed multi-model averaging approach outperformed the available approaches in reducing the root mean square error (RMSE) by 5% and 54% in the wet and dry precipitation conditions, respectively. Therefore, it can be concluded that incorporating the different precipitation extremes in a multi-model combination approach could enhance the assessment of climate change impacts on the climate extremes. The climate change impacts on the extreme events, based on the proposed multi-model ensembles, is thus assessed using the standardized precipitation indexes of 3 month, 6 month, and 12 month durations. In general, the results exhibited that the frequency of wet events increases, whereas that of drought events decreases.

Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility.

Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges); however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RF, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-FR-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926); therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas.

Rainfall trend and variability in Southeast Florida: Implications for freshwater availability in the Everglades.

Freshwater demand in Southeast Florida is predicted to increase over the next few decades. However, shifting patterns in the intensity and frequency of drought create considerable pressure on local freshwater availability. Well-established water resources management requires evaluating and understanding long-term rainfall patterns, drought intensity and cycle, and related rainfall deficit. In this study, the presence of rainfall monotonic trends was analyzed using linear regression and Mann-Kendal trend tests. Pettit's single point detection test examined the presence of an abrupt change of rainfall. Drought in Southeast Florida is assessed using the Standardized Precipitation Index (SPI) in 3-, 6-, 12-, and 24-months scale; and the Fast Fourier Transform is applied to evaluate the frequency of each drought intensity. There was an increase of rainfall in most of the wet season months, the total wet season, and the annual total. The wet season duration showed a decrease driven by a decrease in October rainfall. Since 1990, wet season and total annual rainfall exhibited an abrupt increase. The SPI analysis has indicated that extended wetness characterizes the contemporary rainfall regime since 1995, except for the incidence of intermittent dry spells. Short-term droughts have 3-year to 5-year recurrence intervals, and sustained droughts have a 10-year and 20-year recurrence intervals. In Southeast Florida, prolonged drought limits freshwater availability by decreasing recharge, resulting in a longer hydro-period to maintain the health of the Everglades Ecosystem, and to control saltwater intrusion. The increasing dry season duration suggests the growing importance of promoting surface water storage and demand-side management practices.

A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment.

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.

Erosion and Sediment Transport Modelling in Shallow Waters: A Review on Approaches, Models and Applications.

The erosion and sediment transport processes in shallow waters, which are discussed in this paper, begin when water droplets hit the soil surface. The transport mechanism caused by the consequent rainfall-runoff process determines the amount of generated sediment that can be transferred downslope. Many significant studies and models are performed to investigate these processes, which differ in terms of their effecting factors, approaches, inputs and outputs, model structure and the manner that these processes represent. This paper attempts to review the related literature concerning sediment transport modelling in shallow waters. A classification based on the representational processes of the soil erosion and sediment transport models (empirical, conceptual, physical and hybrid) is adopted, and the commonly-used models and their characteristics are listed. This review is expected to be of interest to researchers and soil and water conservation managers who are working on erosion and sediment transport phenomena in shallow waters. The paper format should be helpful for practitioners to identify and generally characterize the types of available models, their strengths and their basic scope of applicability.

Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS.

This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels' benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning.

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water's surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).

Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran.

Effective management and sustainable development of groundwater resources of arid and semi-arid environments require monitoring of groundwater quality and quantity. The aim of this paper is to develop a reasonable methodological framework for producing the suitability map for drinking water through the geographic information system, remote sensing and field surveys of the Andimeshk-Dezful, Khozestan province, Iran as a semi-arid region. This study investigated the delineation of groundwater potential zone based on Dempster-Shafer (DS) theory of evidence and evaluate its applicability for groundwater potentiality mapping. The study also analyzed the spatial distribution of groundwater nitrate concentration; and produced the suitability map for drinking water. The study has been carried out with the following steps: i) creation of maps of groundwater conditioning factors; ii) assessment of groundwater occurrence characteristics; iii) creation of groundwater potentiality map (GPM) and model validation; iv) collection and chemical analysis of water samples; v) assessment of groundwater nitrate pollution; and vi) creation of groundwater potentiality and quality map. The performance of the DS was also evaluated using the receiver operating characteristic (ROC) curve method and pumping test data to ensure its generalization ability, which eventually, the GPM showed 87.76% accuracy. The detailed analysis of groundwater potentiality and quality revealed that the 'non acceptable' areas covers an area of about 1479km(2) (60%). The study will provide significant information for groundwater management and exploitation in areas where groundwater is a major source of water and its exploration is critical to support drinking water need.

Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida.

In this study, principal component analysis (PCA), factor analysis (FA), and the absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, 15years (2000-2014) dataset of 12 water quality variables covering 16 monitoring stations, and approximately 35,000 observations was used. The PCA/FA method identified five and four potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules and causes were explained. The APCS-MLR apportioned their contributions to each water quality variable. Results showed that the point source pollution discharges from anthropogenic factors due to the discharge of agriculture waste and domestic and industrial wastewater were the major sources of river water contamination. Also, the studied variables were categorized into three groups of nutrients (total kjeldahl nitrogen, total phosphorus, total phosphate, and ammonia-N), water murkiness conducive parameters (total suspended solids, turbidity, and chlorophyll-a), and salt ions (magnesium, chloride, and sodium), and average contributions of different potential pollution sources to these categories were considered separately. The data matrix was also subjected to PMF receptor model using the EPA PMF-5.0 program and the two-way model described was performed for the PMF analyses. Comparison of the obtained results of PMF and APCS-MLR models showed that there were some significant differences in estimated contribution for each potential pollution source, especially in the wet season. Eventually, it was concluded that the APCS-MLR receptor modeling approach appears to be more physically plausible for the current study. It is believed that the results of apportionment could be very useful to the local authorities for the control and management of pollution and better protection of important riverine water quality.

A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.