Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran).

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and - 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to - 344, - 332.8, and - 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.

Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level.

Groundwater level prediction is important for effective water management. Accurately predicting groundwater levels allows decision-makers to make informed decisions about water allocation, groundwater abstraction rates, and groundwater recharge strategies. This study presents a novel model, the self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM), for groundwater level prediction. The SATCN-LSTM model combines the advantages of the SATCN and LSTM models to overcome the limitations of the LSTM model. By utilizing skip connections and self-attention mechanisms, the SATCN model addresses the vanishing gradient problem, identifies relevant data, and captures both short- and long-term dependencies in time series data. By demonstrating the improved performance of the SATCN-LSTM model in terms of mean absolute error and root mean square error (RMSE), and by comparing these results with those reported in previous papers, we have highlighted the advancements and contributions of the proposed model. By improving prediction accuracy, the SATCN-LSTM model enables decision-makers to make informed choices regarding water allocation, groundwater abstraction rates, and drought preparedness. The SATCN-LSTM model contributes to the sustainable and efficient use of groundwater resources by providing reliable information for decision-making processes. The SATCN-LSTM model combines the temporal convolutional network (TCN) architecture with LSTM. TCN is known for its ability to capture short-term dependencies in time series data, while LSTM is effective at capturing long-term dependencies. By integrating both architectures, the SATCN-LSTM model can capture the complex temporal relationships at different scales, leading to improved prediction accuracy. Meteorological data were used to predict GWL. The SATCN-LSTM model outperformed the other models. The SATCN-LSTM model had the lowest mean absolute error (MAE) of 0.09, followed by the self-attention (SA) temporal convolutional network (SATCN) model with an MAE of 0.12. The SALSTM model had an MAE of 0.16, while the TCN-LSTM, temporal convolutional network (TCN), and LSTM models had MAEs of 0.17, 0.22, and 0.23, respectively. The SATCN-LSTM model had the lowest root mean square error of 0.14, followed by SATCN with an RMSE of 0.15. The study results indicated that the SATCN-LSTM model was a robust tool for predicting groundwater level.

Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models.

Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex and challenging, so robust tools are needed. The main aim of this study is to predict wheat yield using an advanced ensemble model. A multilayer perceptron model (MLP) was combined with optimization algorithms to determine MLP parameters as the first step in the study. Several optimization algorithms were used as optimizers, including Particle Swarm Optimization (PSO), Honey Badger Algorithms (HBA), Sine-Cosine Algorithms (SCA), and Shark Algorithms (SA). Meteorological data were inserted into models. Next, the outputs of optimized MLP models were incorporated into an inclusive multiple MLP model (IMM). A new hybrid gamma test was used to determine the most appropriate input combination. A hybrid gamma test was created by coupling the HBA with GT. This paper introduces a robust IMM model, develops an MLP model using optimization algorithms, develops a new hybrid gamma test, uses Generalized Likelihood Uncertainty Estimation (GLUE) to analyze uncertainty, and presents a spatial map of wheat yield prediction. Based on the Gamma Test analysis, mean air temperature (Ta), wind speed (WS), relative humidity (RH), evapotranspiration (ET), and precipitation (P) were the most important input parameters for reliable and accurate winter wheat yield predictions. At the testing level, the IMM model decreased the mean absolute error (MAE) of the MLP-HBA, MLP-SCA, MLP-SA, MLP-PSO, and MLP models by 47%, 52%, 55%, 58%, and 61%, respectively. In the study, the uncertainty of models based on input data was significantly lower than that of the model parameters. In addition, the GLUE analysis revealed that the wheat yield predictions were more stable and confident by considering the ensemble IMM technique. The pattern of root mean square error (RMSE) maps demonstrated that higher error produces in the northeast of Urmia Lake. The developed framework provides insight into rainfed yield responses to weather conditions and is simple and inexpensive. Accurate and reliable wheat yield prediction is essential for agricultural monitoring and food policy analysis.

Rainfall prediction using multiple inclusive models and large climate indices.

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables.

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models.

Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR-vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D50/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D50/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.

Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.

Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.

A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization.

The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.

Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms.

The transient storage model (TSM) is a common approach to assess solute transport and pollution modeling in rivers. Several formulas have been developed to estimate TSM parameters. This study develops a new hybrid optimization algorithm consisting of the dragonfly algorithm and simulated annealing (DA-SA) algorithms. This robust method provides accurate formulas for estimating TSM parameters (e.g., kf, T, [Formula: see text]). A dataset gathered by previous scholars from several rivers in the USA was used to assess the proposed formulas based on several error metrics ([Formula: see text] and [Formula: see text]) and visual indicators. According to the results, DA-SA-based formulas adequately estimated the [Formula: see text] ([Formula: see text], [Formula: see text]), [Formula: see text] ([Formula: see text] [Formula: see text]), and [Formula: see text] ([Formula: see text] [Formula: see text]) parameters. Moreover, the DA-SA-1 showed higher accuracy by improving the RMSE and MAE by 98% compared to the DA and DA-SA-1 as alternatives. The formulas developed in this study significantly outperformed the results of previously proposed models by enhancing the NSE up to 70%. The hybrid DA-SA algorithm method proved highly reliable models to estimate the TSM parameters in the water pollution routing problem, which is vital for reactive solute uptake in advective and transient storage zones of stream ecosystems.

Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test.

The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty.

Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.

Correction to: Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm.

Following the publication of the article it has come to the authors' attention that the first panel of Fig. 11 has been repeated with the second panel of Fig. 11.

Diffusion charging measurements on exhaust solid particle number and lung deposited surface area of compressed natural gas and diesel buses.

Because of their direct contact with society, urban buses are prioritized targets for air quality improvement. In this study, a sample group of in-use urban old buses powered by compressed natural gas (CNG) and diesel engines was chosen for particle emission analysis. The CNG buses do not have any type of after-treatment, while diesel ones are equipped with a diesel particulate filter (DPF). To measure the lung deposited surface area (LDSA), a possible physical metric of exhaust particles' toxicity, a diffusion charger-based analyzer was utilized. The measurements were done at different engine speeds in stationary conditions. The results revealed that although the particle mass emission of CNG buses remains at a low level, the number of emitted particles for 75% of the CNG buses (depending on their maintenance conditions) is 10 to 100 times more than the retrofitted diesel ones, with the range of 106 to 107 p/cm3. The rest 25% of the CNG buses were performing the same as the retrofitted diesel ones in terms of exhaust particle number in the range of 105 p/cm3. In addition, the lowest LDSA parameter at low idle engine speed was measured to be 97.8 and 229.4 µm2/cm3 for a CNG and a DPF retrofitted diesel bus, respectively. This result indicates the same and even lower LDSA and surface area and thus the lower possible toxic potentiality of exhaust particles of CNG buses compared to diesel vehicles at DPF downstream. Investigation on the different behavior of the CNG buses in the emission of particles showed the correlation of some aging parameters such as lubricant oil aging mileage with the released particles and the importance of periodic maintenance interval. Graphical abstract.

Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization.

The scarcity of freshwater causes the necessity for water delineation of brackish water. Reverse osmosis (RO) is one of the popular strategies characterized with lower cost and simple processing procedure compared to the other desalination techniques. The current research is conducted to investigate the efficiency the RO process based on one-week advance prediction of total dissolved solids (TDS) and permeate flow rate for Sistan and Bluchistan provinces located in Iran region. The water parameters including pH, feed pressure temperature, and conductivity are used to construct the prediction matrix. A newly hybrid data-intelligence (DI) model called multilayer perceptron hybridized with particle swarm optimization (MLP-PSO) is developed for the investigation. The potential of the proposed MLP-PSO model is validated against two predominate DI models including support vector machine (SVM) and M5Tree (M5T) models. The results evidenced the potential of the proposed MLP-PSO model over the SVM and M5T models in predicting the TDS and permeate flow rate. In addition, the proposed model attained lower uncertainty for the simulated data. Overall, the feasibility of the hybridized MLP-PSO achieved remarkable predictability for the RO process.

Association of A-197G polymorphism in interleukin-17 gene with chronic periodontitis: Evidence from six case-control studies with a computational biology approach.

AIM: The aim of the present study was to evaluate the association of interleukin-17 (IL-17) A-197G gene polymorphism with chronic periodontitis (CP) in a case-control study, a meta-analysis, and an in silico approach. METHODS: In the case-control study, 122 cases with CP and 126 healthy controls were recruited; IL-17 A-197G genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism. In the meta-analysis, comprehensive literature retrieval was performed on valid databases to identify relevant studies. Bioinformatics tools were employed to investigate the effects of A-197G transition on the promoter region of IL-17. RESULTS: Our case-control study revealed a significant association between IL-17 A-197G transition and CP. The overall meta-analysis revealed significant associations between the IL-17 A-197G polymorphism and CP risk in homozygote co-dominant and recessive models. The stratified analysis also showed a statistically significant association between the mentioned transition and CP risk in the Caucasian population. The in silico analysis revealed that the A-197G polymorphism could make changes in protein-binding sites of the IL-17 promoter region. CONCLUSIONS: Our study supports that IL-17 A-197G transition could be a genetic risk factor for CP. However, further studies with a larger sample size among different ethnicities are required to obtain a more accurate conclusion.

An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration.

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.

Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.