Effect of source-varying input data on erosion potential model performance

This paper examines the performance of the Erosion Potential Method (EPM or Gavrilovi?) and the model response to input data variations caused by choosing different sources of information for the same parameter. The research presented addresses the input data uncertainty via an analysis of the two model input parameters (the soil protection coefficient and the soil erodibility coefficient). The parameter uncertainty analysis is performed following two different approaches: uncertainty analyses of both the selected sample size and the entire population are conducted for an erosion assessment case study of the Dubra?ina River catchment, Croatia. The analysis indicated that, when changing the data source, significant changes in the model outcome values can occur (up to 47% for this case study). Future method modifications should consider the mitigation of these two parameters by potentially making structural changes in the model and therefore moderating the effect.     

Predicting Total Dissolved Gas Concentration on a Daily Scale Using Kriging Interpolation,Response Surface Method and Artificial Neural Network: Case Study of Columbia River Basin Dams,USA

Natural Resources Research - Total dissolved gas (TDG) is an important factor for aquatic life and can cause gas bubble trauma in fish if the concentration is higher than 110%. Dissolved gas is...     

Predictability performance enhancement for suspended sediment in rivers:Inspection of newly developed hybrid adaptive neuro-fuzzy system model

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accur...     

Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations

Evapotranspiration estimation is of crucial importance in arid and hyper-arid regions, which suffer from water shortage, increasing dryness and heat. A modeling study is reported here to cross-station assessment between hyper-arid and humid conditions. The derived equations estimate ET₀ values based on temperature-, radiation-, and mass transfer-based configurations. Using data from two meteorological stations in a hyper-arid region of Iran and two meteorological stations in a humid region of Spain, different local and cross-station approaches are applied for developing and validating the derived equations. The comparison of the gene expression programming (GEP)-based-derived equations with corresponding empirical-semi empirical ET₀ estimation equations reveals the superiority of new formulas in comparison with the corresponding empirical equations. Therefore, the derived models can be successfully applied in these hyper-arid and humid regions as well as similar climatic contexts especially in data-lack situations. The results also show that when relying on proper input configurations, cross-station might be a promising alternative for locally trained models for the stations with data scarcity.

     

The potential of different ANN techniques in evapotranspiration modelling

The potential of three different artificial neural network (ANN) techniques, the multi‐layer perceptrons (MLPs), radial basis neural networks (RBNNs) and generalized regression neural networks (GRNNs), in modelling of reference evapotranspiration (ET₀) is investigated in this paper. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN techniques so as to estimate ET₀ obtained using the FAO‐56 Penman–Monteith (PM) equation. In the first part of the study, a comparison is made between the estimates provided by the MLP, RBNN and GRNN and those of the following empirical models: The California Irrigation Management Information System (CIMIS) Penman (1985), Hargreaves (1985) and Ritchie (1990). In this part of the study, the empirical models are calibrated using the standard FAO‐56 PM ET₀ values. The estimates of the ANN techniques are also compared with those of the calibrated empirical models. Mean square errors, mean absolute errors and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the MLP and RBNN techniques could be employed successfully in modelling the ET₀ process. In the second part of the study, the potential of ANN techniques and the empirical methods in ET₀ estimation using nearby station data is investigated. Among the models, the calibrated Hargreaves model is found to perform better than the others. Copyright © 2007 John Wiley & Sons, Ltd.     

Investigation of RS and GIS techniques on MPSIAC model to estimate soil erosion

Soil erosion due to surface water is a standout among the serious threat land degradation problem and an hazard environmental destruction. The first stage for every kind of soil conservation planning is recognition of soil erosion status. In this research, the usability of two new techniques remote sensing and geographical information system was assessed to estimate the average annual specific sediments production and the intensity erosion map at two sub-basins of DEZ watershed, southwest of Lorestan Province, Iran, namely Absorkh and Keshvar sub-basins with 19,920 ha, using Modified Pacific Southwest Inter-Agency Committee (MPSIAC) soil erosion model. At the stage of imagery data processing of IRS-P6 satellite, the result showed that an overall accuracy and kappa coefficient were 90.3% and 0.901, respectively, which were considered acceptable or good for imagery data. According to our investigation, the study area can be categorized into three level of severity of erosion: moderate, high, and very high erosion zones. The amount of specific sediments and soil erosion predicted by MPSIAC model was 1374.656 and 2396.574 m³ km^?2 year^?1, respectively. The areas situated at the center and south parts of the watershed were subjected to significant erosion because of the geology formation and ground cover, while the area at the north parts was relatively less eroded due to intensive land cover. Based on effective of nine factors, the driving factors from high to low impact included: Topography > Land use > Upland erosion > Channel erosion > Climate > Ground cover > Soil > Runoff > Surface geology. The measured sediment yield of the watershed in the hydrometric station (Keshvar station) was approximately 2223.178 m³ km^?2 year^?1 and comparison of the amount of total sediment yield predicted by model with the measured sediment yield indicated that the MPSIAC model 38% underestimated the observed value of the watershed.     

Impurity effect on clear water evaporation: toward modelling wastewater evaporation using ANN,ANFIS-SC and GEP techniques

The growing shortage of freshwater resources and increasing environmental awareness give rise to the use of treated wastewater as an alternative resource for water supply. Accurate estimation of wastewater evaporation (WWE), as the main cause of water losses, is necessary for proper water resources management. Unfortunately, few studies have focused on modelling WWE despite its vital importance. This study investigates the ability of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) techniques to estimate WWE as a function of variables including wastewater properties, clear water evaporation and climatic factors. The study uses measured data from an experiment conducted in Neishaboor municipal wastewater treatment plant, Iran. Results indicate that the ANN model is superior among the three methods, and also demonstrates higher accuracy when compared with those of a dimensional analysis model using the F-test statistic.