Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Abstract

Accurate simulation of evaporation plays an important role in the efficient management of water Resources. Generally, evaporation is measured using the direct method where Class A panevaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012-2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott's Index and the Nash-Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.

Spatial and Temporal Patterns of the Extreme Precipitation across the Tibetan Plateau (1986–2015)

“…In contrast, the order of decline for the other precipitation indices moved in the opposite direction: Forest, alpine meadow, alpine steppe, and desert steppe ( Figures 6 and 7). Previous research verified significant positive correlations between precipitation and evapotranspiration (ET) [35,65,66], and normalized difference vegetation index (NDVI) [67]. Forests are distributed mainly in the western Sichuan Province, southeast Tibetan, and the northern Yunnan Province with deep ravine regions [35].…”

“…Most of extreme precipitation indices for these ecosystems showed significantly positive trends under the control of enhance NAO activity [55]. Additionally, the elevation of alpine steppe and desert steppe is higher than forest, the increasing trends of most extreme precipitation indices might have been caused by more melting snow and an accelerated hydrological cycle, as more rapid warming occurred at higher elevations than in lower elevations [65]. Comparisons of extreme precipitation indices in different ecosystems have shown that the mean CDD in the forest was lower than that in other regions, but the average value of precipitation was the largest.…”

Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

“…On the other hand, smart computational techniques have emerged and evolved as powerful and advanced approaches that can resolve highly complex relatedmodeling topics (Amirian, Dejam, & Chen, 2018;Hemmati-Sarapardeh, Ghazanfari, Ayatollahi, & Masihi, 2016;Hemmati-Sarapardeh et al, 2018;Hobold & da Silva, 2019;Nait Amar & Zeraibi, 2018;Nait Amar, Zeraibi, & Redouane, 2018a;Nait Amar, Zeraibi, & Redouane, 2018b;Redouane, Zeraibi, & Amar, 2018;Shahsavar, Khanmohammadi, Karimipour, & Goodarzi, 2019;Xi, Gao, Xu, Zhao, & Li, 2018). Among the successful examples of soft computing techniques applications, we can cite production forcasting in thermal enhanced oil recovery (Amirian, Leung, Zanon, & Dzurman, 2015;Amirian, Fedutenko, Yang, Chen, & Nghiem, 2018), optimization of enhanced oil recovery techniques (Nait Amar & Zeraibi 2019), reservoir flood control (Chuntian & Chau, 2002), hydrology (Chau, 2017;Wu & Chau, 2011;Yaseen, Sulaiman, Deo, & Chau, 2019), and meteorology related topics (Ali Ghorbani, Kazempour, Chau, Shamshirband, & Ghazvinei, 2018;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018). More recently, Esmaeili et al (Esmaeili et al, 2019b) applied least square support vector machine (LSSVM) to model the dependency of oil -water relative permeability on temperature.…”

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

“…The ANN is first proposed by McCulloch and Pitts (1943) and followed by many researchers (e.g. Ghorbani et al 2018;Nguyen et al 2018; Van-Dung et al 2018;Binh Thai et al 2019). However, the first person that suggested this technique to be suggested for training a problem was Hebb (1949).…”