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Purpose The purpose of this paper is to investigate the implementation of the targeted subsidies plan in the rural and agricultural sectors of Iran and its impact on the government’s sales income, operating cash flow (OCF) and receivables collection ratio. Design/methodology/approach Using the panel data approach, the authors examine their hypotheses on a sample of six provinces of Iran, including Khorasan Razavi, Khorasan Jonoubi, Kerman, Semnan, Kermanshah and Kurdistan, during 2009-2013. Findings The findings indicate that the implementation of the targeted subsidies plan leads to increased actual electricity sales in the rural sector. Further, while the coefficient on OCF in the estimated model suggests a significant and positive relationship between the OCF and the implementation of the targeted subsidies plan, the coefficient on receivables collection ratio demonstrates a significant but negative association. Contrary to the government’s primary expectations, the results do not provide any support for the reduction of electricity consumption. Originality/value The current study is apparently the first study which conducted on the subject under study.

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This study focuses on diagnosing drainage problems in the coastal areas of Iran by using geostatistical methods, support vector machines (SVMs) and the adaptive neuro‐fuzzy inference system (ANFIS). Groundwater level (WD) and quality were monitored at 37 shallow wells scattered over a 25 000 ha area at different times. Using prepared raster maps of pH, ESP, EC and WD by the best method, drainage problems were categorized into eight classes. Both SVM and ANFIS models significantly improved predicted data for pH, ESP, EC and WD compared with geostatistical models, while SVMs provided slightly better results which were used for further analysis. More than 60% of the area needs drainage to lower the groundwater table in pre‐planting and post‐harvest periods, while during the growing seasons, more than about 72% of the area requires drainage for salinity control. Based on the results, identifying drainage problems at basin scale is possible with cost‐efficient machine learning models with minimum time and data requirements and investment for detailed field surveys. Copyright © 2017 John Wiley & Sons, Ltd.