GMDH algorithms applied to turbidity forecasting

Abstract

By applying the group method of data handling algorithm to self-organization networks, we design a turbidity prediction model based on simple input/output observations of daily hydrological data (rainfall, discharge, and turbidity). The data are from a field test site at the Chiahsien Weir and its upper stream in Taiwan, and were recorded from May 2000 to December 2008. The model has a regressive mode that can assess the estimated error, i.e., whether a threshold has been exceeded, and can be adjusted by updating the field input data. Consequently, the model can achieve accurate estimations over long-term periods. Test results demonstrate that the 2006 turbidity prediction model was selected as the best predictive model (RMSE = 5.787 and CC = 0.975) because of its ability to predict turbidity within the acceptable error range and 90 % required confidence interval (50NTU). 70(3,1,1) is the optimum modeling data length and variable combinations.

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

“…GMDHNN is successfully applied in diverse engineering applications [31][32][33][34]. Within hydrology and water resources related research, Najafzadeh et al [35] developed the GMDHNN model for scour depth (SD) of pipelines estimation due to waves variability; the prediction of local SD at bridge abutments in coarse sediments with thinly armored beds was conducted by Najafzadeh et al [36]; simulation of flow discharge of straight compound channels was reported by Najafzadeh and Zahiri [37]; prediction of significant wave height was established by Shahabi et al [38]; prediction of turbidity considering daily rainfall and discharge data was determined by Tsai and Yen [39]; an improved modeling of the discharge coefficient for triangular labyrinth lateral weirs was described by Parsaie and Haghiabi [40]; an evaluation of treated water quality in a water treatment plant was carried out by Alitaleshi and Daghbandan [41]; a prediction of turbidity and the free residual aluminum of drinking water was tested by Daghbandan et al [42]. Based on the reported literature review, only one study reported the implementation of the GMDHNN ET 0 modeling developed by da Silva Carvalho and Delgado [43].…”

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

“…The GMDH external criterion preserves superior neurons within each layer for successive generations, yielding an optimum neural network structure. 19 The structure of the GMDH network is developed based on the predefined criterion, which discards noneffective nodes using a layer-by-layer pruning process.…”

“…The original vectors are used to build the first neural network layer, using an iterative polynomial regression procedure, with each layer feeding its output vectors to the next layer. The GMDH external criterion preserves superior neurons within each layer for successive generations, yielding an optimum neural network structure …”

Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling

“…In both the cases, GMDH has performed better than reported references. Apart from this in past one decade, GMDH has been used for forecasting wind speed [42] , reservoir water levels [43] , daily traffic flow [44] , stock indices [45] , significant wave height [46] , turbidity [47] , industry market demand [48] , cash demand in ATMs [49] , local vehicle population [50] and even oil prices [51] . In the field of disease forecasting, GMDH has been recently used to predict the number of patients with lower respiratory disease due to air pollution [52] and total number of knee and hip replacements in arthritis patients [53] but it has yet not been used to predict the size of an epidemic.…”