Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Abstract

Abstract.A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

“…Heddam [50] introduced a new ANN-based model, namely, evolving fuzzy neural network, as a capable approach for DO simulation in a river ecosystem. The suitability of fuzzy-based models has been investigated in many studies [51]. Adaptive neuro-fuzzy inference system (ANFIS) is another potent data mining technique that has been discussed in many studies [52][53][54].…”

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

“…ANN model uncertainties come from the choice of ANN architecture (i.e., number of hidden layers, number of neurons, choice of activation function, type of training algorithm and data partitioning), as well as the performance metric chosen. Due to the data-driven nature of these models, propagating these uncertainties is easier [78,79]. One method of quantifying this uncertainty is by using fuzzy numbers to quantify the total uncertainty in the weights, biases and output of the ANN [78,80,81].…”

Artificial Intelligence (AI) Studies in Water Resources

“…Recent studies Dam or lake water level Hipni et al, 2013;Üneş et al, 2015;Li et al, 2016 Evaporation and evapotranspiration Goyal et al, 2014;Karimi et al, 2016;Güçlü et al, 2017Rainfall-runoff Talei et al, 2013Darras et al, 2015;Londhe et al, 2015;Chithra & Thampi, 2016 Sediment Demirci and Baltaci, 2013;Güner and Yumuk, 2014;Droppo & Krishnappan, 2016;Talebi et al, 2016Streamflow Cigizoglu, 2003Huang et al, 2004;Nourani et al, 2012;Ashrafi et al, 2017 Water quality variables Ay, 2010;Akkoyunlu et al, 2011;Ay & Kisi, 2011;Ay & Kisi, 2012;Ay & Kisi, 2013a;Ay & Kisi, 2013b;Kisi & Ay, 2013;Ay, 2014;Ay & Kisi, 2014;Chang et al, 2014;Alizadeh & Kavianpour, 2015;Khan & Valeo, 2015;Ay & Kisi, 2017…”