Abstract After wheat, rice is one of the most important agricultural products in the world, and Iran has a special position here with annual production of more than 2 million t of rice. Evaluation of crop yield has an important role in agricultural policy making due to different conditions and restrictions. Estimating rice yield is a key factor in food security. Any change in the effective parameters can cause changes in rice yield and therefore the food security of the population will be affected. In this study, rice crop yield was estimated by artificial neural networks (ANNs) and ANN‐genetic programming (GP) in 2011 and 2015. Rainfall, permeability, soil texture, land type, evapotranspiration and inlet and inflow and outflow water to paddy lands were used as inputs. The results showed that the ANN‐GP with a root mean square error (RMSE = 80.8 kg ha‾¹) and a correlation coefficient (CC = 0.91) was more accurate than the stand‐alone ANN (with RMSE = 139 kg ha‾¹ and CC = 0.67). Finally, the effect of each input parameter on rice yield was evaluated. Irrigation, drainage and soil type parameters had the best impact rank, with 36, 28 and 31%, respectively. Therefore, the proposed method can act as an efficient tool in estimating rice yield and help decision makers to manage and develop the agricultural system.

10.1002/ird.2566

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Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment

Agricultural Water Management (2020)

Ahmed Elbeltagi Jinsong Deng Ke Wang Anurag Malik Saman Maroufpoor

Irrigation scheduling

Water balance

Water cycle

10.1016/j.agwat.2020.106334

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Citations (88)

Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches

International Journal of Climatology (2019)

Saman Maroufpoor Hadi Sanikhani Özgür Kişi Ravinesh C. Deo Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Wind speed is an essential component that needs to be determined accurately, especially over long‐term periods for various engineering and scientific purposes including renewable energy productions, structural building sustainability and others. In this study, six different heuristic methods: multi‐layer perceptron artificial neural networks, (ANN), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), generalized regression neural networks (GRNN), gene expression programming (GEP) and multivariate adaptive regression spline (MARS) are developed to model monthly wind speeds using meteorological input information. The atmospheric pressure, temperature, relative humidity and rainfall values are obtained from Jolfa and Tabriz meteorological stations, Iran, and are used to build the proposed predictive models.. Different statistical indicators are computed to evaluate and comprehensively assess the performance of the six heuristic methods. Over the testing phase, the ANFIS‐GP and GRNN models are seen to exhibit the highest predictive performance for the Jolfa and Tabriz stations, respectively. That is, the maximum coefficient of determination are found to be 0.874, 0.858, 0.850, 0.849, 0.847 and 0.826, for the GRNN, ANFIS‐GP, ANFIS‐SC, ANN, GEP and MARS models, respectively, for Jolfa station, respectively, revealing the superiority of GRNN over the five counterpart models. The results show the generalization capability of the tested heuristic artificial intelligence techniques for both study stations, and therefore could be explored for windspeed prediction and various decisions made in regards to climate change studies.

Gene expression programming

Perceptron

10.1002/joc.6037

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Citations (28)