A simple framework for selection of water quality models

Chinyama, Annatoria; Ochieng, George M.; Nhapi, Innocent; Otieno, Fred

doi:10.1007/s11157-013-9321-3

Cited by 13 publications

(13 citation statements)

References 7 publications

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“…However, more than 100 water‐quality models have been developed since the Streeter and Phelps models, and a reliable model selection criterion is still lacking (Wang et al, ). Model outputs differ based on study objectives and the type of water environment being simulated (Chinyama, Ochieng, Nhapi, & Otieno, ; Wang et al, ). The models we reviewed simulated the transport and levels of a range of determinates across the study area (Table ).…”

Section: Resultsmentioning

confidence: 99%

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

et al. 2018

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Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.

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Section: Resultsmentioning

confidence: 99%

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

et al. 2018

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“…A particular point of disagreement relates to the valuation of models or scoring, a term used in MCDA to refer to the evaluation of the models in each criterion. Some authors [18,[20][21][22] claim that the scoring (and the whole model selection process, including choice of criteria and which models to evaluate) should be carried out exclusively by end-users, for transparency reasons and to reduce time and costs of the model selection stage. Chinyama et al [21], for instance, suggested that model users can score the models on the criteria based on a literature review on the models.…”

Section: Valuation Of Modelsmentioning

confidence: 99%

“…Within the literature on water quality model selection, there is a fair degree of similarity between the process of aggregating values from different criteria. Most methods consider eliminatory criteria, setting a minimum base level so that, if not satisfied, the model is excluded from the process [18][19][20][21][22]. No additional guidance is provided to select one model out of the remaining adequate models (Figure 1).…”

Section: Aggregation Proceduresmentioning

confidence: 99%

“…Only in the last 15 years have new complete frameworks for water quality model selection started to appear, guiding the whole process of model selection, including the definition of which characteristics of the models are being compared (i.e., defining the criteria of comparison) and how to compare these [18,19,21,22]. Some approaches [18] identify a set of questions to guide the definition of criteria to be used as a means of comparison between models.…”

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confidence: 99%

“…In this particular study, modelers then evaluated the different models under those criteria. In a more recent protocol for model selection [21], the main aim was to provide a framework to assist inexperienced model users, as well as to provide an auditable process. Although not explicitly identified, this protocol is based on a Multi-Criteria Decision Analysis (MCDA) process.…”

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ScoRE—A Simple Approach to Select a Water Quality Model

Mateus

Vieira

Almeida

et al. 2018

Water

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Over the past decades, water quality models have become unique tools in the management of aquatic resources. A consequence of their widespread application is the significant number of models now available. Available methodologies to compare models provide limited support for their choice in the first place, especially to end-users or modelers with limited experience. Here we propose a method to assist in the selection of a particular model from a set of apparently similar models. The method is termed ScoRE, as it grades models according to three main aspects: Scope (aim, simulated processes, constituents, etc.), Record (reference to the model in publications, its range of applications, etc.), and the Experience of using the model from the user perspective (support material, graphical user interface, etc.). End-users define the criteria to be evaluated and their relative importance, as well as the conditions for model exclusion. The evaluation of models is still performed by the modelers, in open discussion with end-users. ScoRE is a complete approach, as it provides guidance not only to exclude models but also to select the most appropriate model for a particular situation. An application of this method is provided to illustrate its use in the choice of a model. The application resulted in the definition of 18 criteria, where 6 of these were defined exclusively by the end-users. Based on these and the relative importance of each criterion, ScoRE produced a ranking of models, facilitating model selection. The results illustrate how the contributions from modelers and end-users are integrated to select a model for a particular task.

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Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

Anmala

Venkateswarlu

2021

Water Environment Research

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Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land‐use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. Practitioner points The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R2 value, RMSE and MAE indicate the effectiveness of DTs in prediction.

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A simple framework for selection of water quality models

Cited by 13 publications

References 7 publications

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

ScoRE—A Simple Approach to Select a Water Quality Model

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

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