Streamflow forecasting using heuristic machine learning methods

Streamflow forecasting is vital for designing and managing water resources systems. This study evaluates the prediction accuracy of two heuristic methods, artificial neural network-genetic algorithm (ANN-GA) and adaptive neurofuzzy inference system-genetic algorithm (ANFIS-GA) in streamflow prediction using monthly streamflow data of Neelum and Kunhar Rivers of Pakistan. The prediction capability of two methods are tested using the different time lags input combinations using statistical indicators and compared with M5 Regression Tree (M5RT) model. In results, it is found that ANN-GA and ANFIS-GA provided better prediction accuracy than M5RT model. Addition of month number showed a positive effect of periodicity on the prediction accuracy of models.

[1]  Alban Kuriqi,et al.  Calibration of channel roughness in intermittent rivers using HEC-RAS model: case of Sarimsakli creek, Turkey , 2019, SN Applied Sciences.

[2]  L. Garrote,et al.  Flow regime aspects in determining environmental flows and maximising energy production at run-of-river hydropower plants , 2019 .

[3]  A. Dashti,et al.  H-2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS , 2017 .

[4]  Ozgur Kisi,et al.  Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation , 2019, Energies.

[5]  Özgür Kisi,et al.  Pan evaporation modeling using four different heuristic approaches , 2017, Comput. Electron. Agric..

[6]  Mamdouh El Haj Assad,et al.  Comparison of artificial intelligence methods in estimation of daily global solar radiation , 2018, Journal of Cleaner Production.

[7]  Ozgur Kisi,et al.  Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models , 2019, Meteorological Applications.

[8]  Sandeep K. Sood,et al.  Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm , 2020, J. Exp. Theor. Artif. Intell..

[9]  Hajir Karimi,et al.  Application of artificial neural network–genetic algorithm (ANN–GA) to correlation of density in nanofluids , 2012 .

[10]  Ozgur Kisi,et al.  Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm , 2017 .

[11]  Majid Amidpour,et al.  A hybrid artificial neural network and genetic algorithm for predicting viscosity of Iranian crude oils , 2014 .

[12]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[13]  Vipul Jain,et al.  Forecasting municipal solid waste generation using artificial intelligence models—a case study in India , 2019, SN Applied Sciences.

[14]  H. Pourghasemi,et al.  Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. , 2018, The Science of the total environment.

[15]  O. Kisi,et al.  Long-Term Trends and Seasonality Detection of the Observed Flow in Yangtze River Using Mann-Kendall and Sen’s Innovative Trend Method , 2019, Water.

[16]  Manish Kumar Goyal,et al.  Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression , 2014, Water Resources Management.

[17]  Ozgur Kisi,et al.  Modeling reference evapotranspiration using three different heuristic regression approaches , 2016 .

[18]  Mohammad Mehrabi,et al.  Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide , 2019, Geomatics, Natural Hazards and Risk.

[19]  O. Kisi.  Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree , 2015 .

[20]  V. Singh,et al.  Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model , 2017 .

[21]  Pejman Tahmasebi,et al.  A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation , 2012, Comput. Geosci..

[22]  Arash Adib,et al.  Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions , 2017 .

[23]  S. Nazif,et al.  Monthly prediction of streamflow using data-driven models , 2019, Journal of Earth System Science.

[24]  Huiping Zhang,et al.  Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process , 2011, Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering.

[25]  Madan Mohan Tripathi,et al.  A novel GA-ANFIS hybrid model for short-term solar PV power forecasting in Indian electricity market , 2019, Journal of Information and Optimization Sciences.

[26]  O. Kisi,et al.  Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs , 2020 .

[27]  Noel E. O'Connor,et al.  A Low-Cost Smart Sensor Network for Catchment Monitoring , 2019, Sensors.

[28]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[29]  Ozgur Kisi,et al.  Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review , 2014 .

[30]  J. P. King,et al.  Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, Southern New Mexico , 2016, Expert Syst. Appl..

[31]  Sinan Jasim Hadi,et al.  Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination , 2018, Journal of Hydrology.

[32]  Amin Asadi,et al.  Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid , 2019, Materials.

[33]  Lifeng Wu,et al.  Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China , 2019, Journal of Hydrology.

[34]  Ozgur Kisi,et al.  Daily streamflow prediction using optimally pruned extreme learning machine , 2019, Journal of Hydrology.

[35]  André Gustavo da Silva Melo Honorato,et al.  Monthly streamflow forecasting using neuro-wavelet techniques and input analysis , 2018, Hydrological Sciences Journal.

[36]  A. Seifi,et al.  Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models , 2018, Environmental Science and Pollution Research.

[37]  Zaher Mundher Yaseen,et al.  Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation , 2018, Water Resources Management.

[38]  Mohammad Sharifi,et al.  Application of hybrid adaptive neuro-fuzzy inference system in well placement optimization , 2018 .

[39]  Ali Rahimikhoob,et al.  Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment , 2014, Water Resources Management.

[40]  Maysam F. Abbod,et al.  Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction , 2012, Adv. Fuzzy Syst..