Journal Article

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models

Nyarko, Emmanuel Karlo, Wu, Shiqiang, Zhu, Senlin, Heddam, Salim, Hadzima-Nyarko, Marijana, Piccolroaz, Sebastiano

2019

Overview

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature ( T a ), river flow discharge ( Q ), and the components of the Gregorian calendar ( CGC ) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors ( T a , Q , and the CGC ) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only T a is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Adaptive systems / Air temperature / Algorithms / Aquatic Pollution / Artificial intelligence / Artificial neural networks / Atmospheric Protection/Air Quality Control/Air Pollution / Chemical reactions / Cluster Analysis / Clustering / Computer simulation / Earth and Environmental Science / Ecotoxicology / Environment / Environmental Chemistry / Environmental Health / Environmental Monitoring / Environmental science / Fuzzy Logic / Fuzzy systems / Hydroelectric power / Hydrologic models / Hydrology / Inference / Learning algorithms / Machine Learning / Model accuracy / model validation / Models, Chemical / Multilayer perceptrons / Neural networks / Neural Networks, Computer / Organic chemistry / Regulated rivers / Research Article / River flow / River regulations / River systems / river water / Rivers / Rivers - chemistry / Temperature / Temperature effects / Waste Water Technology / Water / Water Management / Water Pollution Control / water power / Water Quality / Water temperature