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Total dissolved solid prediction is an important factor which can support the early warning of water pollution, especially in the areas exposed to a mixture of pollutants. In this study, a new fuzzy-based intelligent system was developed, due to the uncertainty of the TDS time series data, by integrating optimization algorithms. Monthly-timescale water quality parameters data from nearly four decades (1974–2016), recorded over two gaging stations in coastal Iran, were used for the analysis. For model implementation, the current research aims to model the TDS parameter in a river system by using relevant biochemical parameters such as Ca, Mg, Na, and HCO3. To produce more compact networks along with the model’s generalization, a hybrid model which integrates a fuzzy-based intelligent system with the grasshopper optimization algorithm, NF-GMDH-GOA, is proposed for the prediction of the monthly TDS, and the prediction results are compared with five standalone and hybrid machine learning techniques. Results show that the proposed integrated NF-GMDH-GOA was able to provide an algorithmically informed simulation (NSE = 0.970 for Rig-Cheshmeh and NSE = 0.94 Soleyman Tangeh) of the dynamics of TDS records comparable to the artificial neural network, extreme learning machine, adaptive neuro fuzzy inference system, GMDH, and NF-GMDH-PSO models. According to the results of sensitivity analysis, Sodium in natural bodies of water with maximum value of error (RMSE = 56.4) had the highest influence on the TDS prediction for both stations, and Mg with RMSE = 43.251 stood second. The results of the Wilcoxon signed rank tests also indicated that the model’s prediction means were different, as the p value calculated for the models was less than the standard significance level (α=0.05).

Total dissolved solid prediction is an important factor which can support the early warning of water pollution, especially in the areas exposed to a mixture of pollutants. In this study, a new fuzzy-based intelligent system was developed, due to the uncertainty of the TDS time series data, by integrating optimization algorithms. Monthly-timescale water quality parameters data from nearly four decades (1974–2016), recorded over two gaging stations in coastal Iran, were used for the analysis. For model implementation, the current research aims to model the TDS parameter in a river system by using relevant biochemical parameters such as Ca, Mg, Na, and HCO3. To produce more compact networks along with the model’s generalization, a hybrid model which integrates a fuzzy-based intelligent system with the grasshopper optimization algorithm, NF-GMDH-GOA, is proposed for the prediction of the monthly TDS, and the prediction results are compared with five standalone and hybrid machine learning techniques. Results show that the proposed integrated NF-GMDH-GOA was able to provide an algorithmically informed simulation (NSE = 0.970 for Rig-Cheshmeh and NSE = 0.94 Soleyman Tangeh) of the dynamics of TDS records comparable to the artificial neural network, extreme learning machine, adaptive neuro fuzzy inference system, GMDH, and NF-GMDH-PSO models. According to the results of sensitivity analysis, Sodium in natural bodies of water with maximum value of error (RMSE = 56.4) had the highest influence on the TDS prediction for both stations, and Mg with RMSE = 43.251 stood second. The results of the Wilcoxon signed rank tests also indicated that the model’s prediction means were different, as the p value calculated for the models was less than the standard significance level (α=0.05).

Today, using systems based on renewable resources is a suitable alternative to fossil fuels. However, due to problems such as the lack of access in all the times needed to supply cargo and high-investment cost, it has not been well-received. Therefore, in this research, the modeling of the photovoltaic system with battery storage has been done to supply the required load, and various scenarios have been evaluated in terms of economic parameters and reliability indicators of the studied system for a better understanding of the comparison indicators. It has been evaluated from two modes, one connected to the network and one disconnected from the network. One of the important results is the supply of 56% of the load by the photovoltaic cell in the presence of the grid, which, in this scenario, the electrical load is supplied by the photovoltaic cell and the grid is 164.155 kWh/yr and 128.504 kWh/yr, respectively.

Today, using systems based on renewable resources is a suitable alternative to fossil fuels. However, due to problems such as the lack of access in all the times needed to supply cargo and high-investment cost, it has not been well-received. Therefore, in this research, the modeling of the photovoltaic system with battery storage has been done to supply the required load, and various scenarios have been evaluated in terms of economic parameters and reliability indicators of the studied system for a better understanding of the comparison indicators. It has been evaluated from two modes, one connected to the network and one disconnected from the network. One of the important results is the supply of 56% of the load by the photovoltaic cell in the presence of the grid, which, in this scenario, the electrical load is supplied by the photovoltaic cell and the grid is 164.155 kWh/yr and 128.504 kWh/yr, respectively.

Refereed/Peer-reviewed The dark side of the consumption of Natural Resources (NTR) is the carbon dioxide emissions (CO2) resulting from the extraction of NTR as well as the increasing level of consumption within the economy. Besides, the massive expansion and growth of the Chinese economy have also meant its CO2 is at an all-time high, thus making China one of the leading contributors to global warming. Hence the current study fills a knowledge vacuum by analyzing the relationships governing NTR and technological innovation (TIN) in countering the environmental damage due to CO2, by revisiting the EKC in the Chinese context through “Quantile Autoregressive Distributed Lag” (QARDL). The results validate the EKC where NTR consumption worsens the environment further; however, TIN is a potential solution in countering CO2. In accordance with the findings, it is recommended that government institutions need to ensure the preservation of NTR without conceding the economic benefits and prosperity.

Refereed/Peer-reviewed The dark side of the consumption of Natural Resources (NTR) is the carbon dioxide emissions (CO2) resulting from the extraction of NTR as well as the increasing level of consumption within the economy. Besides, the massive expansion and growth of the Chinese economy have also meant its CO2 is at an all-time high, thus making China one of the leading contributors to global warming. Hence the current study fills a knowledge vacuum by analyzing the relationships governing NTR and technological innovation (TIN) in countering the environmental damage due to CO2, by revisiting the EKC in the Chinese context through “Quantile Autoregressive Distributed Lag” (QARDL). The results validate the EKC where NTR consumption worsens the environment further; however, TIN is a potential solution in countering CO2. In accordance with the findings, it is recommended that government institutions need to ensure the preservation of NTR without conceding the economic benefits and prosperity.

AbstractAimPalms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change.LocationTropical and subtropical moist forests.Time periodCurrent.Major taxa studiedPalms (Arecaceae).MethodsWe assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure.ResultsOn average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work.ConclusionsTree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests.