Different Climates Of Iran Research Articles

A holistic approach for performance evaluation of wastewater treatment plants: integrating grey water footprint and life cycle impact assessment.

Wastewater treatment plants (WWTPs) have positive and negative impacts on the environment. Therefore, life cycle impact assessment (LCIA) can provide a more holistic framework for performance evaluation than the conventional approach. This study added water footprint (WF) to LCIA and defined ϕ index for accounting for the damage ratio of carbon footprint (CF) to WF. The application of these innovations was verified by comparing the performance of 26 WWTPs. These facilities are located in four different climates in Iran, serve between 1,900 and 980,000 people, and have treatment units like activated sludge, aerated lagoon, and stabilization pond. Here, grey water footprint (GWF) calculated the ecological impacts through typical pollutants. Blue water footprint (BWF) included the productive impacts of wastewater reuse, and CF estimated CO2 emissions from WWTPs. Results showed that GWF was the leading factor. ϕ was 4-7.5% and the average WF of WWTPs was 0.6 m3/ca, which reduced 84%, to 0.1 m³/ca, through wastewater reuse. Here, wastewater treatment and reuse in larger WWTPs, particularly with activated sludge had lower cumulative impacts. Since this method takes more items than the conventional approach, it is recommended for integrated evaluation of WWTPs, mainly in areas where the water-energy nexus is a paradigm for sustainable development.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

The impact of facade geometry on visual comfort and energy consumption in an office building in different climates

In recent years, there has been a heightened emphasis improving visual comfort and energy efficiency. Various solutions have been explored to achieve high-performance design. Shading devices play a crucial role in enhancing building performance by redusing solar gains, excessive daylight, and improving both energy efficiency and occupants' visual comfort. This research aims to investigate the effect of facade geometry on visual comfort and energy consumption in four different climates of Iran and categorize each variable based on effectiveness for each location. Parametric office modeling was done by using Grasshopper and Rhino software. Then, the effect of the facade on the interior lighting and energy consumption was analyzed by Radiance, Daysim, and EnergyPlus calculation engines. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) was selected to optimize solutions, minimize energy consumption, maximize useful daylight illuminance, and view quality. In addition, the methodology was used to explore the framework for optimizing office facade design in Iran's diverse climatic zones. The simulation results indicate that window-to-wall ratio and inclined wall were essential for balancing daylighting performance and energy consumption. This research stated that using a self-shading design could increase the quality of view up to 75% while reducing energy consumption and the risk of glare. Results proposed a design framework to improve visual comfort and save energy. The rotating façade's wall 10°-30° reduced cooling energy demand and energy usage intensity in selected models. So, an inclined wall could be an efficient shading device to improve building's performance in Iran.

An applicable multi-generation system for different climates from energy, exergy, exergoeconomic, economic, and environmental (5E) perspectives

Proposing a practical multi-generational system for different climates can be the key for setting up renewable-based systems as an alternative to fossil fuels in different countries. This paper introduced a flexible renewable energy system that is capable of generating multiple forms of energy using solar energy and hydrogen storage, and can operate under diverse weather conditions across four cities in Iran. The system was equipped with a solar unit, photovoltaic panels (PV), heating and cooling units, a desalination technology, a hydrogen production unit, and a fuel cell to supply heating, cooling, electricity and freshwater loads for a residential building. Mashhad (normal), Tabriz (cold), Bandar Abbas (humid), and Yazd (hot and dry) cities were considered as different climates in Iran to suggest the best city based on the present system. Analysis and simulation were conducted, taking into account five perspectives, namely energy, exergy, exergoeconomic, economic, and environmental (5E). This was done utilizing the TRNSYS and Engineering Equation Solver (EES) packages. Multi-objective optimization was carried out to achieve the best performance using the TOPSIS method for the four cities mentioned. The system was grid-connected to sell excess power when there is no need for electricity and receive power when it is needed. Optimization showed that Bandar Abbas has the highest exergy efficiency of the system by 26.3 % and the lowest total cost rate of the whole components by 6.08 $/h. Moreover, the maximum net output power of 28.72 kW was reported in Tabriz. Environmental analysis revealed that the system had the ability to lower CO2 emissions through the generation of clean electricity and avoiding the use of natural gas. This was particularly significant in Bandar Abbas and Yazd, where the maximum reductions in CO2 emissions were obtained at 23.3 tons and 4.4 tons, respectively. In conclusion, the payback period for Mashhad, Tabriz, Bandar Abbas, and Yazd cities was obtained at 6.5 years, 5.8 years, 5.5 years, and 5 years, respectively.

Techno-Econo-Enviro Assessment of Photovoltaic-Thermal (PV-T) System for Residential Use in Iran Based on Köppen Climate Classification

The reason for utilizing PV-T systems is their capability to generate both electricity and heat at the same time, which enhances energy efficiency and decreases the need for multiple energy sources. Considering the potential of solar energy and different climates in Iran, in this research, the feasibility, modeling, and comparison of the PV-T system to supply electricity and heat to residential units were investigated in 28 cities with 9 climates of Iran. A thorough evaluation of performance was conducted using the software Polysun 9.0 and PVsyst 6.6.8 for a duration of one year. The scientific novelty of this study relies on the simultaneous utilization of two analytical software, the thoroughness of the research, and taking into account the impact of climate on the findings. Investigations showed that the best performance of the designed system is related to Bandar Abbas and Zahedan, and the worst performance is related to Rasht and Sari. In general, according to the amount of electricity and heating produced by the PV-T system, it showed that Shiraz and Rasht stations have the highest and lowest production of energy. In terms of environmental effects, Birjand station will have the most and Rasht the least reduction in CO2 emissions. In terms of heating energy demand for the investigated building, Ardabil has the highest amount and Bandar Abbas has the lowest amount.

Finding the Best Station to Use Buildings Integrated Photovoltaics (BIPVs) in Eight Different Climates of Iran: Effect of Wind Speed and Photovoltaic Modules Type

Buildings consume a significant amount of energy worldwide. Therefore, providing a part of this energy consumption with renewable energy, and reducing energy consumption, can lead to reducing pollutants. Due to the importance of the abovementioned points, this work aims to supply part of the required electricity in the buildings by using solar cells in eight climates of Iran. According to the available photovoltaic modules in the Iranian market, the study has been performed for six different types of solar cells. Considering the effect of temperature and wind speed on the efficiency of solar cells has made the results of the present work more consistent with reality. The simulations are performed for 25 years of the project’s useful life using HOMER V2.81 software, and the studied system is connected to the grid. The conclusions displayed that the Passive Emitter Rear Cell Silicon (perc-si) has the highest efficiency (21.22%–22.8%). Marand station is economically suitable (total net present cost (NPC) of 3180$ for a polycrystalline panel), and Gonbad station is appropriate because of high solar energy production (9183 kWh/year) and low environmental pollution (12602.9 kg/year). Due to the diversity and breadth of Iran’s climates, the results of the present study can be used for other areas with similar climates.

Energy modeling to compensate for the seasonal lack of electrical and thermal energy depending on the different climates of Iran

Renewable energy sources are in focus for environment-friendly power generation when compared to non-renewable sources. Modeling an energy system of a statistical population can shed light on the possibilities and potential of using renewable resources. In this study, energy modeling of 4 provinces of Iran with different climates is done for 2020 and 2032. The lack of energy caused by seasonal climatic impacts is compensated for by using renewable energy systems. The modeling of three different scenarios is considered to indicate different policies in each energy system strategy. The energy system's past data is gathered and analyzed to predict future data, and then the 2032 energy system is modeled using EnergyPLAN. The results show that there will be a shortage of electrical energy in summers in hot & humid and hot & dry climates, while the energy shortage for cold and temperate & humid climates is the heating demand in winters. Three scenarios of business as usual (BAU), using maximum possible renewable energy (S1), and changing the structure of the energy supply system (S2) are considered with their specification. The results indicate that by using S1, 61.42 TWh of primary energy sources (PES), and by using S2, 136.7 TWh of PES consumption is reduced. Also, for the same scenarios, 29.98 Mt less CO2 is emitted for all climates. The climatic analysis illustrates that using solar in hot & humid and hot & dry, wind and geothermal in cold, and hydropower in hot & humid and temperate & humid climates produce the most amount of renewable potential which not only compensates the lack of seasonal energy but also replace 8% of the total energy needed, previously supplied by fossil fuels. Totally for the 4 provinces, 3250 MW of hydropower, 5625 MW of solar, 650 MW of wind, and 100 MW of geothermal energy are considered while other provinces with the same climate could benefit too based on their geographical specification.

Modification of standardized precipitation index in different climates of Iran

AbstractThe most popular metric for drought monitoring is the standardized precipitation index (SPI). It does have some drawbacks, though, such as using gamma probability distribution function as the default distribution and not taking into account seasonal variations. The effectiveness of SPI in monitoring droughts in various Iranian climates was examined in this study, and in order to address its limitations, it was contrasted with the modified SPI (mod‐SPI). Then, the performance of the mod‐SPI and SPI in drought monitoring was compared with nine meteorological stations throughout Iran during the period 1956–2010. The results showed that the gamma distribution function was not selected in any of the 12 timescales (TS) considered at the studied meteorological stations. Based on the Cohen's kappa index analysis, there is a clear difference between the SPI and mod‐SPI in monitoring drought classes, which is more evident at low TS (less than 6 months), but at much higher TS (above TS‐9), the two indices almost coincide. The results showed that in the arid and semiarid areas, the SPI and mod‐SPI are significantly different, so it seems that the SPI monitors aridity rather than drought. The results imply that using mod‐SPI instead of SPI provides more accurate drought monitoring by eliminating the seasonal effects of precipitation data. Considering the low efficiency of SPI in short‐term drought monitoring (less than 9 months) in arid and semiarid regions, it is recommended to use mod‐SPI instead of SPI in most parts of Iran, especially on the southern coasts of Iran.

Dynamic simulation and exergy-economic assessment of solar thermal refrigeration systems in different climates

In this paper, the performance of solar-thermal cooling systems for air conditioning application is investigated in different climates of Iran. Diverse climate types are considered which makes the results applicable to other countries. Available commercial refrigeration cycles that are capable of integration with solar-thermal collector systems including the closed liquid absorption cycle, closed solid adsorption cycle, and desiccant cycle are studied. First, a solar and climatic map is proposed and the representative cities have been selected for each climate. Then, the climate parameters related to each region are extracted to create an hourly database. Also, the cooling load of a reference building is modeled based on the climate database, each refrigeration cycle is simulated using thermodynamic equations, and the dynamic model is generalized daily for the hot season in each city. In the last step, each cooling system is examined using an exergy-economic analysis, and technical and economic indicators are compared. The results show that the most suitable solar-thermal refrigeration cycle that can be installed in the central plateau and semi-arid areas of Iran is the closed-cycle liquid absorption system. The annual average exergy-economic factor and the annual solar fraction in this cycle are 0.7578 and 0.57, respectively. In the southern coastal areas, due to the high humidity and air temperature in summer, the solid desiccant refrigeration system with an exergy-economic factor of 0.6978 and a solar fraction of 0.2416 is preferred. The suitable solar-thermal refrigeration cycle in the cold and mountainous regions of Iran is a solid adsorption system with a silica gel absorber. The annual solar fraction and average annual exergy-economic factor in this cycle are 0.5158 and 0.7394. Also, in northern moderate regions, the solid adsorption refrigeration system with an average annual exergy-economic factor of 0.409 and average exergy efficiency of 0.12 is comparatively beneficial.

Feasibility and investigation of residential HVAC system with combined ground source heat pump and solar thermal collectors in different climates of Iran

Geoexchange is one of the most energy-efficient, environmentally clean, and cost-effective space-conditioning systems available on the market. Seasonal storage of solar energy in geothermal boreholes is sharply interesting as a means of heating and cooling in buildings with different applications. In this study, a closed-loop geo-exchange and solar thermal system with two different heat exchangers are examined in an energy-efficient house. TRNSYS, a simulation software tool, was used to model the yearly performance of the hybrid ground-source heat pump (GSHP) system. Two different types of ground heat exchangers, e.g., tube-in-tube and U-tube were employed. The mass flow rate through the heat exchangers and the solar collector was examined to choose the best scenario for each of them. The economic benefits of a gray water heat recovery system and solar collector were studied; the results show that 517 and 536 US Dollars/Year can be saved from each of them, respectively. System performance analysis was carried out in different cities with various climates in Iran. Moreover, a complete economic assessment was evaluated for all cases. The results show that for the same inlet water temperature to GSHP, the tube-in-tube heat exchanger can pass 45% more flow than the U-tube heat exchanger for the same size as the HP. Furthermore, in the absence of the solar collector, a 2.7 × 108 kJ increase was conducted in the auxiliary heater component yearly. Finally, four different climates were evaluated for the launching of the GSHP system with a solar thermal collector. The hot and dry climate with 27 °C fluid inlet to a heat pump, a total borehole depth of 220 m, and an initial cost of $ 25,047 was selected as the most appropriate weather conditions for launching the system in Iran.

Economic optimization of hybrid renewable energy systems supplying electrical and thermal loads of a tourist building in different climates

Due to the high potential of tourism in Iran and the high utilization of tourist buildings in Iran, as well as the high energy consumption in the buildings sector, this study aims to provide a feasible model for designing a renewable energy supply system for a tourist building in different climates of Iran. According to the country’s climate, 5 cities of Ahvaz, Bandar Abbas, Rasht, Mashhad, and Yazd were selected as the cities under study. The hybrid system also consists of photovoltaic panels, wind turbine, battery, and converter for power supply and boilers with natural gas fuel and geothermal heat pump to provide building thermal load. The heat pump is used to supply the load needed to preheat the building’s hot water. The system is connected to the electricity grid, so by selling excess electricity, the net project costs (NPC) will be reduced and the system can provide part of its need from the grid. The scenarios which were studied are of two categories. In the first category, all the thermal load is supplied by the boiler. After selecting the best economic scenario by Homer software in this category, the heat pump is added to the system by calculating the thermal load required to preheat the hot water. The scenarios used two types of wind turbines with a capacity of 10 and 50 kW and two types of 25-W panels with different efficiencies of 15.3% and 18%. Finally, a top-down scenario was chosen for each city. The best city to run the project on economic criteria is Mashhad with an NPC of $ 195,745 and a renewable fraction of $ 50.5. Using a heat pump to preheat the hot water, would also save 7% on fuel consumption and reduce CO2 production by 639,000 kg per year.

Experimental investigation of a PV/T system containing a TEG section between water-based heat exchanger and air-based heat sink

The negative impact of temperature on PV power generation has led to the implementation of new cooling techniques globally to enhance the annual performance of PV systems. This study proposes a novel arrangement of Photovoltaic/Thermal-Thermoelectric generator PV/T-TEG integrated system that uses water and air cooling to enhance the performance of PV modules in warm regions. The system applies TEGs between the water heat exchanger and the air heatsink, instead of between the PV module and the cooling fluid. This avoids the non-uniform cooling associated with direct TEG connection to the PV backside. The proposed system was tested in four different climates in Iran and compared with PV-TEG and alone-PV systems. The results showed that the proposed system reduced the PV surface temperature by 23%, increased the electrical output power by more than 15%, and improved the PV electrical efficiency by about 3%. The average total energy efficiency was 55.6% and the maximum thermal efficiency was about 20%. These values were satisfactory for the non-concentrated TEG-based PV/T system in all analyzed climates.

Assessing climate change impact on river flow extreme events in different climates of Iran using hybrid application of LARS-WG6 and rainfall-runoff modeling of deep learning

Climate change and its side effects on weather variables and water resources is one of the recent international concerns. This research aims to project future changes in weather variables due to climate change for different emission scenarios of RCP2.6, RCP4.5 and RCP8.5 to assess and compare climate change impact on river flow variation based on hybrid application of deep learning and LARS-WG6 in different climates of Iran (CSA: cold semi-arid; HT: humid temperate; CA: cold arid). For this purpose, best combination of daily rainfall and discharges data with different lags were selected as inputs in rainfall-runoff modeling using Convolutional Neural Networks (CNN) model. Predicted meteorological data by the LARS-WG6 in the future (2021-2040) were then used as inputs for the selected CNN model and consequently runoff was predicted. Obtained findings showed that projected rainfall changes in 2021-2040 compared to the base period in scenarios of RCP2.6, 4.5 and 8.5 will be +14, +11, +6% in CA, +8, +1, +2% in CSA and +3.7, -1, -2.6% in HT regions, respectively. Whereas, maximum discharge in CA climate in scenario RCP2.6 is projected to raise about 18%, in the HT climate shows a reduce of about 5% in RCP8.5 and in CSA will decrease by about 5% in RCP2.6. Considering the effect of climate as well as seasonal changes in each region on the amount and direction of the climate change impacts on meteorological and hydrologic variables changes, assessing climate change impact in each region is necessary to extract adaptation strategies in water resources management.

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

To accurately manage water resources, a precise prediction of reference evapotranspiration (ETref) is necessary. The best empirical equations to determine ETref are usually the temperature-based Baier and Robertson (BARO), the radiation-based Jensen and Haise (JEHA), and the mass transfer-based Penman (PENM) ones. Two machine learning (ML) models were used: least squares support vector regression (LSSVR) and ANFIS optimized using the particle swarm optimization algorithm (ANFPSO). These models were applied to the daily ETref at 100 synoptic stations for different climates of Iran. Performance of studied models was evaluated by the correlation coefficient (R), coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI) and the Nash-Sutcliffe efficiency (NSE). The combination-based ML models (LSSVR4 and ANFPSO4) had the lowest error (RMSE = 0.34–2.85 mm d−1) and the best correlation (R = 0.66–0.99). The temperature-based empirical relationships had more precision than the radiation- and mass transfer-based empirical equations.

Investigating the effects of water resources consumption reduction policies on agricultural sustainability in different climates in Iran

Investigating the effects of water resources consumption reduction policies on agricultural sustainability in different climates in Iran

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

Energetic, economic, environmental and climatic analysis of a solar combisystem for different consumption usages with PSI method ranking

Using solar systems is increasing due to oil shortage, environmental effects, and renewability. Solar thermal combisystems use the energy taken to provide for both domestic hot water (DHW) and space heating (SH), which is under investigation by energy researchers. This study uses a dynamic energy analysis of the solar system to evaluate the solar parameters. Also, the environmental and economic effects of using solar systems are discussed. The study is done on four different consumption usages in four different climates of Iran. Energy simulation shows that hot & humid climates, office, and mall usages have the largest solar fraction (minimum of 95.2, 38.5, and 35%, respectively). The lowest optimal collector surface area belongs to a hot and humid climate with 4–21.5 m2 and office usage with 4–159 m2. Also, collector efficiency decreases with higher collector area amounts. 20-year economic analysis shows that the net present value of hospital usage with a maximum of $2216.8, cold climate with a maximum of $768.4, and hot & humid climate with a maximum of $2765.9 are the most affordable. The environmental analysis illustrates that CO2 emission avoidance is highest in hospital usage with a maximum of 1924.5 kg, residential usage with a maximum of 1707.8 kg, and hot & dry climate with a maximum of 1349.2 kg. Lastly, the preference selection index (PSI) method is used to rank all application possibilities, which shows that using systems is highly recommended in cold, hot & humid climates with the best ranks of 1 and 3 and hospital usage with the best rank of 1.

Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian Model Averaging approach

Pan evaporation (Ep) is an efficient and practical tool for planning and managing water resources, understanding the water balance in hydrological processes, and developing irrigation systems, particularly in regions with limited water resources. Accurate prediction of Ep using easy-to-measure meteorological variables is beneficial for any region. The current study aimed to explore the applicability of Copula-based Bayesian Model Averaging (CBMA) for improving probabilistic Ep predictions in different climates of Iran. To this end, the parameters of the Adaptive Neuro-Fuzzy Interface System (ANFIS) were optimized using four meta-heuristic algorithms of Seagull Optimization Algorithm (SOA), Crow search Algorithm (CA), Firefly Algorithm (FFA), and Particle Swarm Optimization (PSO) for finding global solutions. The ANFIS-SOA, ANFIS-CA, ANFIS-FFA, ANFIS-PSO, and ANFIS models were implemented as inputs for employing ensemble CBMA and Bayesian Model Averaging (BMA) methods. Daily meteorological variables of average air temperature (Ta), sunshine hours (SH), relative humidity (RH), wind speed (WS), and Ep from six stations from 2000 to 2003 were applied. Evaluation of five Posterior Distribution Functions (PDFs) and three copula functions showed that Gumbel marginal distribution and Gumbel–Hougaard copula function provide the smallest Kolmogorov–Smirnov statistic indicator at the 5% significance level for all predictive models. The results established that the ensemble CBMA approach exhibited the highest prediction accuracy in all climates, followed by the BMA model, superior to the other individual models. The average root mean square error (RMSE) of the ensemble CBMA model was lower than BMA, ANFIS-SOA, ANFIS-CA, ANFIS-PSO, ANFIS-FFA, ANFIS, by 20.35%, 43.19%, 51.28%, 56.74%, 61.15%, and 64.36%, respectively. Furthermore, the uncertainty analysis indicated the Ep predictions were more confident after applying CBMA. In conclusion, we highly recommend applying the ensemble CBMA model to improve Ep’s prediction performance by standalone and hybrid machine learning models. The combination of Ta-SH was suggested to be used for robust predicting daily Ep in the regions of hot desert, cold desert, and cold semi-arid and Ta-RH in the humid subtropical area regarding the convenience of data acquisition.

Consistency assessment between summer simmer index and other heat stress indices (WBGT and Humidex) in Iran's climates

This study aimed to assess the applicability of summer simmer index (SSI) in different climates of Iran and compare it with other common heat stress indices including WBGT and Humidex. The daily mean air temperature and relative humidity were obtained from the Meteorological Organization of Iran for the 60-year period between 1957 and 2016. Statistical tests including Pearson correlation coefficient and linear regression tests were used to consistency assessment between Summer Simmer Index and wet-bulb globe temperature (WBGT) and Humidex indices. The results of the present study showed that the SSI is significantly correlated with both WBGT and Humidex indices (R2 > 0.932, p < 0.001). In addition, the obtained correlation between SSI and Humidex was higher than correlation between SSI and WBGT in all the climates. Moreover, SSI in the Mediterranean region shows less correlation with both WBGT and Humidex indices (R2 = 0.932 and 0.964 for WBGT and Humidex, respectively). The highly correlation coefficients were observed between SSI and WBGT and Humidex indices, and also between SSI and other environmental parameters; moreover interpreting of SSI is easy, so it can be suggested that SSI can be used as an alternative index for WBGT or Humidex in assessment of heat stress in different climates of Iran.

A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios

A Decision Support System (DSS) is a highly efficient concept for managing complex objects in nature or human-made phenomena. The main purpose of the present study is related to designing and implementation of real-time monitoring, prediction, and control system for flood disaster management as a DSS. Likewise, the problem of statement in the research is correlated to implementation of a system for different climates of Iran as a unique flood control system. For the first time, this study coupled hydrological data mining, Machine Learning (ML), and Multi-Criteria Decision Making (MCDM) as smart alarm and prevention systems. Likewise, it created the platform for conditional management of floods in Iran's different clusters of climates. According to the KMeans clustering system, which determines homogeneity of the hydrology of a specific region, Iran's rainfall is heterogeneous with 0.61 score, which is approved high efficiency of clustering in a vast country such as Iran with four seasons and different climates. In contrast, the relation of rainfall and flood disaster is evaluated by Nearest Neighbors Classification (NNC), Stochastic Gradient Descent (SGD), Gaussian Process Classifier (GPC), and Neural Network (NN) algorithms which have an acceptable correlation coefficient with a mean of 0.7. The machine learning outputs demonstrated that based on valid data existence problems in developing countries, just with verified precipitation records, the flood disaster can be estimated with high efficiency. In the following, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method as a Game Theory (GT) technique ranked the preventive flood damages strategies through three social (Se 1), environmental (Se 2), and economic (Se 3) crises scenarios. The solutions of flood disaster management are collected from literature review, and the opinion approves them of 9 senior experts who are retired from a high level of water resource management positions of Iran. The outcomes of the TOPSIS method proved that National announcement for public-institutional participation for rapid response and funding (G1-2), Establishment of delay structures to increase flood focus time to give the animals in the ecosystem the opportunity to escape to the upstream points and to preserve the habitat (G 2–8), and Granting free national financial resources by government agencies in order to rebuild sensitive infrastructure such as railways, hospitals, schools, etc. to the provincial treasury (G3-10) are selected as the best solution of flood management in Social, Environmental, and Economic crises, respectively. Finally, the collected data are categorized in Social, Environmental, and Economic aspects as three dimensions of Sustainable Development Goals (SDGs) and ranked based on the opinion of 32 experts in the five provinces of present case studies.