In this study, a model on the basis of artificial neural networks is developed to predict the peak horizontal acceleration. The neural network model provides an objective analysis method which requires neither specifying predictive functional forms nor the independence of the inside variables. The Joyner and Boore data set (BSSA, Vol. 71, pp. 2011-2038, 1981, was used for analysis. For comparison, one-and two-step regression procedures were also applied to the same data set. Various fitness criteria have been considered. Finally, the proposed procedure showed an agreeable capability for the required prediction of ground motion parameters.

This paper introduces the narrow strip irrigation (NSI) method and aims to estimate water-use efficiency (WUE) and yield in apple orchards under NSI in the Miandoab region located southeast of Lake Urmia using a machine learning approach. To perform the estimation, a hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and seasons optimization (SO) algorithm was proposed. According to the irrigation and climate factors, six different models have been proposed to combine the parameters in the SO-ANFIS. The proposed method is evaluated on a test data set that contains information about apple orchards in Miandoab city from 2019 to 2021. The NSI model was compared with two popular irrigation methods including two-sided furrow irrigation (TSFI) and basin irrigation (BI) on benchmark scenarios. The results justified that the NSI model increased WUE by 1.90 kg/m3 and 3.13 kg/m3, and yield by 8.57% and 14.30% compared to TSFI and BI methods, respectively. The experimental results show that the proposed SO-ANFIS has achieved the performance of 0.989 and 0.988 in terms of R2 criterion in estimating WUE and yield of NSI irrigation method, respectively. The results confirmed that the SO-ANFIS outperformed the counterpart methods in terms of performance measures.

Various shapes of weirs, such as rectangular, trapezoidal, circular, and triangular plan forms, are used to adjust and measure the flow rate in irrigation networks. The discharge coefficient (Cd) of weirs, as the key hydraulic parameter, involves the combined effects of the geometric and hydraulic parameters. It is used to compute the flow rate over the weirs. For this purpose, a hybrid ISADE-SVR method is proposed as a hybrid model to estimate the Cd of sharp-crested W-planform weirs. ISaDE is a high-performance algorithm among other optimization algorithms in estimating the nonlinear parameters in different phenomena. ISaDE algorithm is used to improve the performance of SVR by finding optimal values for SVR's parameters. To test and validate the proposed model, the experimental dataset of Kumar et al. and Ghodsian were utilized. Six different input scenarios are presented to estimate the Cd. Based on the modeling results, the proposed hybrid method estimates the Cd in terms of the H/P, Lw/Wmc, and Lc/Wc. For the superior method, R2, RMSE, MAPE, and δ are obtained as 0.982, 0.006, 0.612, and 0.843, respectively. The amount of improvement in compared with GMDH, ANFIS and SVR is 3.6%, 1.2%, 1.5% in terms of R2.

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m3, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m3/ha, which suggests a decrease of 5019.20 and 2509.6 m3/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.

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The use of urban treated wastewater for agriculture is one of the most important parts of unconventional water use in arid and semi-arid regions, but the proper situation for its application needs to be considered. For this purpose, a study aimed at comparing five levels of water requirement including well water (control) (T1), urban treated wastewater (T2), 50% well water combination and 50% urban treated wastewater (T3), alternating irrigation between well water and urban treated wastewater each watering (T4), and combination of 33% well water and 66% urban treated wastewater (T5) in a randomized complete block design with three replications on water use efficiency and cotton yield. The study was conducted in a selected farm located in Torbat-Heydarieh southeastern Iran during two cropping years (2013 and 2014). Then, a hybrid tree growth optimization algorithm (TGO) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict cotton yield from four independent variables: soil characteristics, well water irrigation, urban treated wastewater irrigation, and meteorological data. Experimental treatments significantly altered soil chemistry. Cottonseed weight, cotton yield, and the number of bolls increased during the second year of treatments. A Duncan’s test of the mean showed that T3 significantly outperformed the other treatments measured as cottonseed weight, cotton yield, number of bolls, and water use efficiency. Overall, treatments utilizing treated wastewater outperformed the control, irrigation with well water. Additionally, based on the modeling results irrigation with an equal ratio of the well and treated wastewater resulted in improving soil and cotton growth conditions and yield during the study.

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