Developing an ANN Based Streamflow Forecast Model Utilizing Data-Mining Techniques to Improve Reservoir Streamflow Prediction Accuracy: A Case Study

Abstract

This study illustrates the benefits of data pre-processing through supervised data-mining techniques and utilizing those processed data in an artificial neural networks (ANNs) for streamflow prediction. Two major categories of physical parameters such as snowpack data and time-dependent trend indices were utilized as predictors of streamflow values. Correlation analysis of different models indicate that, for the period of January to June, using fewer predictors led to simpler modeling with equivalent accuracy on daily prediction models. This did not hold in all periods. For monthly prediction models, accuracy was improved compared to earlier works done to predict monthly streamflow for the same case of Elephant Butte Reservoir (EB), NM. Overall, superior prediction performance was achieved by utilizing datamining techniques for pre-processing historical data, extracting the most effective predictors, correlation analysis, extracting and utilizing combined climate variability indices, physical indices, and employing several developed ANNs for different prediction periods of the year.

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

“…The results showed that the forecasting models are sensitive to different environmental input variables. AI-based models, especially ANN techniques, were frequently applied for forecasting purposes with streamflow and environmental processes thanks to easy implementation, low computational cost and suitable performance (Fotovatikhah et al, 2018;Motahari & Mazandaranizadeh, 2017;Olyaie, Banejad, Chau, & Melesse, 2015;Wang, Xu, Chau, & Lei, 2014;Zamanisabzi, King, Dilekli, Shoghli, & Abudu, 2018). They have been developed to predict a water quality index (Gazzaz, Yusoff, Aris, Juahir, & Ramli, 2012), monthly chemical oxygen demand concentration (Khalil, Awadallah, Karaman, & El-Sayed, 2012), daily water temperature, salinity and dissolved oxygen (Alizadeh & Kavianpour, 2015), etc.…”

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

“…In order to accomplish these tasks, a variety of techniques and approaches can be applied, such as rule-based systems (RBS), genetic algorithms, cellular automata, Fuzzy Systems, Multiagent systems, Swarm Intelligence, Case-based reasoning (CBR), and Artificial Neural Networks (ANN) (Chen et al, 2008). For example, AI (particularly genetic algorithms, Artificial Neural Networks, and Deep Learning) has been applied in a variety of civil engineering contexts including optimum design of structures (Hajela and Berke, 1991;Adeli and Park, 1995;Camp et al, 2003;Hadi, 2003), concrete strength modeling (Yeh, 1999;Ni and Wang, 2000;Lee and Ahn, 2003;Al-Salloum et al, 2012), predicting geotechnical settlement and liquefaction (Shahin et al, 2002;Young-Su and Byung-Tak, 2006), earthquake engineering (Lee and Han, 2002;Arslan, 2010;Yilmaz, 2011), concrete design mix (Jayaram et al, 2009), prediction and forecasting of water resources and flooding (Maier and Dandy, 2000;Mitra et al, 2016;Alexander et al, 2018;Lin et al, 2018;Yu et al, 2018;Zamanisabzi et al, 2018;Li et al, 2019), water quality and sediment modeling (Nagy et al, 2002;Zhang et al, 2010;Barzegar et al, 2016;Sabouri et al, 2016), irrigation and water-delivery scheduling (Nixon et al, 2001;Karasekreter et al, 2013), rainfallrunoff modeling (Minns and Hall, 1996;Tokar and Johnson, 1999;Cheng et al, 2005Cheng et al, , 2017Dixon, 2005;Jeong and Kim, 2005;…”

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“…Modern computer-network software often uses algorithms inspired by nature. For example, I refer to all those protocols for the optimization of multi-variable problems known as genetic algorithms, which exploit the rules of genetics to solve mathematical problems with many independent variables, or neural networks, mathematical systems that base the calculation on a "learning" database that the system has previously prepared 7,8 .…”