Modeling and Monitoring of Drought for forecasting it, to Reduce Natural hazards Atmosphere in western and north western part of Iran, Iran

Safarianzengir, Vahid; Sobhani, Behrouz; Asghari, Sayad.

doi:10.1007/s11869-019-00776-8

Cited by 37 publications

(15 citation statements)

References 26 publications

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“…In this step, artificial neural networks, created by modeling the functioning system of the human brain, process experimental data without regard to the physics of the problem, and extract the law behind this information. Compared to conventional models, these models require less input and less computational effort (Malczewski, 2007;Kianian et al, 2014;Safarianzengir et al, 2019). A fuzzy system is a system based on the logic rules of "condition-result" which, using the concept of linguistic variables and the fuzzy decision-making process, depicts the space of input variables over the space of output variables (Ahmadzadeh et al, 2010;Afrouzi et al, 2011).…”

Section: Adaptive Neuro-fuzzy Inference System and Neural Network Modelsmentioning

confidence: 99%

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Safarianzengir

Fatahi²,

Doumari

2022

Front. Energy Res.

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Attention to climatic and environmental potentials for exploitation and meeting human needs has been considered for many years. But the optimal and correct use of this potential requires accurate knowledge and subsequent careful planning. Today, the use of climate and productive energy from the environment is more of a concern for researchers. This investigation aims to appraise the feasibility of establishing solar thermal power plants (solar panels) by examining eight climatic and atmospheric parameters in the environment based on fuzzy logic in northwestern Iran. In this study, the weather datum of 22 Urban Climate Synoptic stations were prepared to evaluate the climatic conditions for the feasibility of establishing a thermal solar power plant for sustainable development using fuzzy logic in the north-west of Iran for each synoptic station during a statistical period of 31 years (1990–2020). This research used eight climatic parameters: average max temperature, average temperature, average min relative moisture, mean relative moisture, total monthly precipitation, and a daily average of Sunny hours, altitude, and wind speed in this systemic model. MATLAB software was used to combine the input data from fuzzy logic and ANFIS Adaptive Neural Network. The climate zoning map for establishing a thermal solar power plant with the scores obtained for each of the parameters and finally the final map was drawn using the ArcGIS. The results show that the fuzzy logic method showed a great variety of options for the establishment of solar thermal power plants using climatic parameters. In total, after obtaining the final rating for each of the 22 stations, Bonab station, with a coefficient score of (0.544–0.589), and Miyaneh station with a score (0.543–0.577), located in East Azarbaijan Province, in the north-west of Iran are appropriate for the establishment of a solar power plant. The Northwest of Iran is suitable to meet the needs of various energies, including domestic electricity, due to the potential stemming from its favorable climatic conditions climate; therefore, it is necessary to take steady steps to develop this important industry. Also, the accuracy of MATLAB and ArcGIS software with a high level of 0.96% confidence in finding a suitable place for the establishment of solar power stations was confirmed. According to the final results obtained from the integration and fuzzification of 8 climatic parameters and zoning of suitable and unsuitable areas for prioritization, Ardabil station with a score of 0.345% in the priority of inconvenient location, and Bonab station with a score of 0.589% in the priority of where the best places were for the establishment of solar power stations. Considering the findings of the present study, the central region and distant locales in mountainous areas were suitable places for the establishment of solar panels. The valuable results of the present study can be given more attention by the sectors that use a lot of sustainable energy. The results of this research in the field of sustainable energy production from the environment are more in the spotlight.

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Section: Adaptive Neuro-fuzzy Inference System and Neural Network Modelsmentioning

confidence: 99%

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Safarianzengir

Fatahi²,

Doumari

2022

Front. Energy Res.

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“…The plant is cultivated at a height of about 1,500 m above sea level, the base temperature for H. annuus is about 6°C, and it grows from 10 to 32°C in the mean diurnal temperature. The plant well tolerates the temperature of -5°C at the beginning of germination, then becomes sensitive to cold and freezes at step 6 leaf at -2°C or less (Khajepour, 2016;Safarianzengir et al, 2019). The desirable climate of H. annuus is presented in Table 1.…”

Section: Ecological Needs Of Helianthus Annuusmentioning

confidence: 99%

Assessment and zoning of suitable climate for economic development of cultivation of Sunflower (Helianthus annuus) garden crop (Ardabil Province, Iran)

Fatahi¹,

Zengir²,

Sobhani³

et al. 2022

Europ.J.Hortic.Sci.

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This research was conducted to identify areas of Ardabil province susceptible for Helianthus annuus cultivation. The aim of this study was to identify climatic factors for H. annuus cultivation using TOPSIS method and zoning areas susceptible to cultivating this product using ArcGIS in Ardabil province. In this research, climatic data such as minimum temperature, precipitation, and relative humidity were used along with slope, soil and altitude maps of 10 synoptic stations during 22 years (1994 to 2015). Weighing the layers was carried out using the TOPSIS method and the desirable climatic conditions of the H. annuus product, and a digital map of the data was obtained in the ArcGIS. Positive ideal map, negative ideal and final map of H. annuus cultivation in Ardabil province were prepared by integrating of maps in ArcGIS. The final results showed that about 25.89% of the province's area is without limitation, 22.96% with a low limitation, 26.89% with a moderate limitation, and 24.77% with a severe limitation for H. annuus cultivation. As a result, the cultivation of this product was without limitation in terms of temperature and relative humidity and is faced with limitation in terms of water requirement supply.

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“…Different indicators are used to diagnose drought and wet conditions [22]. However, many studies [23][24][25][26][27] have shown that the standardized precipitation index (SPI) works well for different climates in Iran to assess the frequency and duration of droughts. The index, developed by McKee et al (1993), describes the cumulative probability of precipitation in any given period.…”

Section: Drought and Wet Conditionsmentioning

confidence: 99%

Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran

et al. 2021

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The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions.

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Modeling and Monitoring of Drought for forecasting it, to Reduce Natural hazards Atmosphere in western and north western part of Iran, Iran

Cited by 37 publications

References 26 publications

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Feasibility and Zoning of Establishing Solar Power Stations to Produce Sustainable Energy From the Environment in Northwestern Iran

Assessment and zoning of suitable climate for economic development of cultivation of Sunflower (Helianthus annuus) garden crop (Ardabil Province, Iran)

Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran

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