Individual Artificial Neural Network Research Articles

Hybrid Random Forest Regression and Artificial Neural Networks for Modelling and Monitoring the State of Health of Li-Ion Battery

Lithium-ion batteries play a significant role in modern energy storage systems for a number of applications, including renewable energy installations, electric cars, and portable devices. In order to guarantee the long-term dependability and safety of these battery packs, comprehensive modelling and monitoring approaches are required for accurate State of Health (SoH) evaluation. This research suggests an innovative strategy that combines the strengths of Random Forest Regression (RFR) and Artificial Neural Networks (ANN) for reliable Li-ion battery SoH modelling and monitoring. The research starts with the collection of thorough Li-ion battery data, which includes voltage, current, temperature, and cycling characteristics. The battery dataset's nonlinear relationships are captured utilising RFR as the main modelling method. In terms of feature selection and prediction precision, RFR's ensemble of decision trees shines, making it a strong contender for modelling complex battery behaviour. The SoH prediction is then enhanced by the use of ANN, a deep learning framework renowned for its ability to extract detailed patterns from data. The characteristics of RFR are complemented by ANN's capacity to discover hidden features and nonlinear correlations, improving the overall prediction performance. The suggested hybrid approach, that combines RFR and ANN, is trained and verified utilising a diverse database acquired from Li-ion battery sources under operational circumstances. To assess the prediction accuracy of a suggested framework measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values are employed. The outcomes show that the hybrid RFR-ANN model works better than the individual RFR and ANN models, producing better SoH predictions. A viable solution for real-time Li-ion battery health monitoring is provided by this combination of conventional ML and deep learning approaches, which demonstrates the synergy among complexity and interpretability. This research advances battery SoH assessment and paves the way for the practical incorporation of precise monitoring systems into applications like electric vehicles, where timely and accurate SoH data is essential for maximising battery performance and extending operational lifespan.

Assessment of the split tensile strength of fiber reinforced recycled aggregate concrete using interpretable approaches with graphical user interface

Incorporating recycled concrete aggregates into concrete represents a sustainable approach to mitigate the extraction of natural mineral resources and alleviate the adverse environmental effects associated with the concrete industry. Nevertheless, it encounters challenges due to the vulnerability of the hardened mortar adhering to natural aggregates. Thus, leading to an increased susceptibility to cracking and reduced strength. Therefore, this study utilized machine learning (ML) methods such as gene expression programming (GEP), random forest regression (RFR), artificial neural networks (ANN), ANN-Bagging, and ANN-Boosting models to forecast the split tensile strength (STS) of fiber-reinforced recycled aggregate concrete (FRRAC). To develop the models, 257 data points were gathered from experimental studies, encompassing ten influential input variables and considering the STS as the output variable. The accuracy of the models was assessed using statistical metrics. The five developed prediction models showcased excellent and reliable performance, with an impressive correlation coefficient (R) exceeding 0.80 during training and reaching 0.92 in testing. However, the GEP model outperformed the remaining four models, with an excellent R-value of 0.980 in training and 0.983 in testing. In addition, the standalone ANN model demonstrated performance on par with the ensemble ANN-Boosting model. In contrast, the ANN-Bagging ensemble model outperformed the individual ANN model, showcasing superior prediction accuracy with impressive R-values of 0.952 and 0.975 for training and testing, respectively. Moreover, the feature analysis based on SHapley Additive exPlanations (SHAP) revealed that fiber content, recycled aggregate density, cement, and water have the highest contribution in estimating the STS of FRRAC. Furthermore, a user-friendly graphical interface (GUI) has been developed to simplify the application of machine learning models in predicting the strength properties of concrete. In conclusion, the findings can encourage the utilization of FRRAC in both structural and non-structural reinforced concrete elements. Therefore, promoting the recycling and reuse of construction waste into building materials and thereby reducing environmental impact. Furthermore, incorporating recycled concrete aggregate in construction projects has the potential to decrease construction material costs, as it reduces reliance on natural resources such as natural coarse aggregate.

Taxonomic Analysis of Asteroids with Artificial Neural Networks

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to the limits of ground-based observational instruments. In the near future, the Chinese Space Survey Telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 and 23 mag, respectively. With the aim of analyzing the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus–Binzel taxonomic system, our ANN classification tool composed of five individual ANNs is constructed, and the accuracy of this classification system is higher than 92%. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained by us in 2006 and 2007 with the 2.16 m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering its accuracy and stability, our ANN tool can be applied to analyze CSST asteroid spectra in the future.

Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models

Solar irradiation prediction including Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) is a useful technique for assessing the solar energy potential at specific locations. This study used five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) to predict GHI and DNI in Africa. Additionally, a hybrid model combining MLR and ANNs was proposed to predict both GHI and DNI and improve the accuracy of individual ANN models. Solar radiation (GHI and DNI) and global meteorological data from 85 cities with different climatic conditions over Africa during 2001-2020 were used to train and test the models developed. The Pearson correlation coefficient was used to identify the most influential input variables to predict GHI and DNI. Two scenarios were proposed to achieve the goal, each with different input variables. The first scenario used influential input parameters, while the second incorporated geographical coordinates to assess their impact on solar radiation prediction accuracy. The results revealed that the suggested linear-nonlinear hybrid models outperformed all other models in terms of prediction accuracy. Moreover, the investigation revealed that geographical coordinates have a minimal impact on the prediction of solar radiation.

The use of a set of artificial neural networks to predict added resistance in head waves at the parametric ship design stage

During the parametric ship design stage, added ship resistance, a component of total ship resistance is analysed to estimate the required propulsive power. The determination of added resistance at this design stage is problematic because the added resistance is calculated using hull shape, which is unknown at this stage. At the parametric design stage, only basic ship parameters, such as length, breadth, draught, block coefficient and Froude number are defined and known. Therefore, the focus of this research was to develop an effective tool to solve this problem. The article presents a set of five Artificial Neural Networks (ANNs) to predict added resistance using basic design parameters. Only model test data was used to train the ANNs in order to obtain reliable results. The research showed that using an ANN set consisting of five ANNs developed using different segregated data resulted in slightly more reliable and accurate estimates than using an individual ANN. To facilitate implementation, the developed ANNs were provided in the form of mathematical functions and open source software code from the Mendeley Data repository. A practical example of how an ANN set can be used to solve a design task was demonstrated in this article.

Prediction of Isolated Substorms by a Package of Parallel Neural Networks

A neural network forecast of substorms caused by the impact of solar wind plasma flows on theEarth’s magnetosphere has been performed. For this, recurrent neural network models were created based onphysical cause-and-effect relationships of the dynamics of high-latitude geomagnetic activity (according tothe AL index) with the parameters of the interplanetary magnetic field (IMF) and solar wind plasma (SWP).Two parameters are used as input sequences: the bz-component of the IMF and the integral parameterΣ[NV2], taking into account the prehistory of the process of pumping the kinetic energy of the solar wind intothe magnetosphere, where N and V are the plasma density and solar wind velocity, respectively. The forecastof the AL index according to SWP and IMF for 10 min, etc. with 10 min discreteness individually by an individualartificial neural network (ANN) for each point corresponding to the dynamics of the AL index wascompleted. This means that the prediction of a continuous series of values AL index is achieved by a parallelrunning of the ANN package. The number of ANNs in the package is determined by the duty cycle of therequired predictive series of the AL index, while taking 90 min of the history of input parameters in each ofthe networks into account provides a prediction of the values AL index with an accuracy of ~80%

Intelligent optimization of Reference Evapotranspiration (ETo) for precision irrigation

Reference Evapotranspiration (ETo) is the basis for the conservation of irrigation water for sustainable development in agriculture. The FAO-56 Penman-Monteith (FAO-56 PM) is the standard method of ETo determination that is complex due to the involvement of many parameters. Many machine learning approaches were proposed to simplify ETo determination with limited parameters. The existing machine learning-based solutions rely on a fixed number of limited parameters for the determination of ETo. The study proposed a solution to determine the raw ETo with only temperature and optional optimization of the raw ETo according to the availability of Relative Humidity (RH), Wind Speed (WS), and sunshine duration ratio (n/N). The study proposed an ensembled Artificial Neural Network (ANN) model (Model-1) to determine ETo according to available minimal environmental parameters. The environmental conditions from the year 2001–2021 of Pakistan are used to develop a dataset with the FAO-56 PM method. The unique attribute of the proposed solutions is the flexibility in the use of environmental parameters for ETo determination according to the FAO-56 PM method. The performance of the proposed ensembled model (Model-1) is compared against the individual ANN model with all parameters (Model-2) and with the individual ANN model with only temperature (Model-3). The predictions by the proposed solution from Model-1 are also compared against the FAO-56 PM method. It is observed that the proposed Model-1 is more accurate with 91.44% accuracy, as compared to Model-2 and Model-3. The predictions by the proposed Model-1 are more correlated with the ETo by FAO-56 PM with a Pearson correlation of 0.996 as compared to the ETo predictions by Model-2 and Model-3.

Modeling the Nonlinearities Between Coaching Leadership and Turnover Intention by Artificial Neural Networks

This present work uses artificial neural networks (ANNs) to examine the association between various dimensions of coaching leadership and turnover Intention. The coaching leadership data were collected from 194 employees across multiple schools in Korea. The ANN models are capable of higher predictive accuracy than conventional linear regression analysis. An individual ANN software was developed to predict and evaluate the relative importance of input variables on turnover intention. Furthermore, we identified the nonlinear relationship by performing a sensitivity analysis on the model. Based on the results, we concluded that coaching leadership strongly affects teachers’ attitudes toward not leaving their school. The graphical illustration of results provided strong evidence of nonlinear and complexity, suggesting that ANN models can recognize the relationship between coaching leadership dimensions with turnover Intention.

Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

The modeling and products prediction for biomass oxidative pyrolysis based on PSO-ANN method: An artificial intelligence algorithm approach

In this work, the oxidative pyrolysis of two kinds of biomass was conducted on a fixed bed reactor. The pyrolysis product yields (water, tar, gas and char) as well as two kinds of gaseous species (CO and CO2) were analyzed. Based on the experiments, the artificial neural network (ANN) model was built and trained for product yield prediction. Experimental results show that the reaction mechanism of oxidative pyrolysis was much complicated than inert pyrolysis. With the addition of oxygen, homogenous and heterogenous oxidation of volatiles or nascent char significantly influenced the product distribution. Modeling results showed that the combination transfer function of logsig and purelin for hidden layer was suitable for modeling of biomass oxidative pyrolysis. Individual ANN model had weak prediction performance for the data points located around the boundary of database. This weakness as well as the tendency of local convergence leaded to the decline of prediction accuracy for individual ANN model. Particle swarm optimization (PSO) was further used to optimize the initial weight and threshold value of the ANN model, which significantly increased the prediction accuracy. The relative error of the PSO-ANN model decreased to below 10%, which proved to be a effective tool for modeling of biomass oxidative pyrolysis.

Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

An accurate solar energy forecast is of utmost importance to allow a higher level of integration of renewable energy into the controls of the existing electricity grid. With the availability of data in unprecedented granularities, there is an opportunity to use data-driven algorithms for improved prediction of solar generation. In this paper, an improved generally applicable stacked ensemble algorithm (DSE-XGB) is proposed utilizing two deep learning algorithms namely artificial neural network (ANN) and long short-term memory (LSTM) as base models for solar energy forecast. The predictions from the base models are integrated using an extreme gradient boosting algorithm to enhance the accuracy of the solar PV generation forecast. The proposed model was evaluated on four different solar generation datasets to provide a comprehensive assessment. Additionally, the shapely additive explanation framework was utilized in this study to provide a deeper insight into the learning mechanism of the algorithm. The performance of the proposed model was evaluated by comparing the prediction results with individual ANN, LSTM, and Bagging. The proposed DSE-XGB method exhibits the best combination of consistency and stability on different case studies irrespective of the weather variations and demonstrates an improvement in R2 value of 10%–12% to other models.

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

Short-term solar radiation forecasting using a new seasonal clustering technique and artificial neural network

ABSTRACT Solar radiation represents the most important parameter for sizing and planning solar power systems. However, solar radiation depends significantly on meteorological conditions which are variable and uncontrollable. Therefore, forecasting global solar radiation can play a key role to integrate solar energy resources into the electric grid. This paper presents a new hybrid approach based on seasonal clustering technique and artificial neural network (ANN) for forecasting 1 h-ahead of global solar radiation. For this purpose, the fuzzy c-means algorithm (FCM) was used to cluster 3 years of monthly average experimental data into different seasons according to solar and meteorological parameters of Évora city. Subsequently, based on the seasonal clustering results, the meteorological dataset was divided into dfferent training subsets. Furthermore, for each subset, an ANN model has been designed to forecast hourly global solar radiation. In this study, hourly meteorological data from January 2012 to December 2016 have been used for forecasting. The hourly data were collected from Évora-city’s meteorological station in Portugal (38°34 N, 07°54 W). The results show the superiority of the hybrid approach compared to the individual ANN model according to statistical indicators.

Turbulence closure for high Reynolds number airfoil flows by deep neural networks

The combination of turbulence big data with artificial intelligence is an active research topic for turbulence study. This work constructs black-box algebraic models to substitute the traditional turbulence model by the artificial neural networks (ANN), rather than correcting the existing turbulence models in most of current studies. We mainly focused on flows past airfoils at high Reynolds (Re) numbers. Our previous work has developed a turbulence model for flows at different Mach (Ma) number and angles of attack (AOA) with fixed Re number and achieved satisfying results. Nevertheless, for turbulence with variable Re numbers, the generalization ability of the model can not be enhanced effectively by simply increasing the train data. To model the nonlinearity of various turbulent effects at high Re number, prior knowledge about scaling analysis is integrated into the model design and deep neural networks (DNN) is adopted as the framework. Considering the different scaling characteristics, the flow field is divided into different regions and two individual ANN models are built separately. Besides, the combination of regularization, limiters, and stability training is adopted to enhance the robustness of the proposed model. The results of Spallart-Allmaras (SA) model are used as the datasets and reference to the modeling evaluation. The proposed model is trained by six flows around NACA0012 airfoil and applicative to different free stream conditions and airfoils. It is found that the results calculated by the proposed model, such as eddy viscosity, velocity profile, drag coefficient and so on, agree well with reference data, which validate the generalization ability of the model. This work shows the prospect of turbulence modeling by machine learning methods.

Neural Network Based Pavement Condition Assessment with Hyperspectral Images

Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral angle mapping (SAM) on hyperspectral images. In the proposed method, the resulting ANN prediction outputs are used to make a new prediction along with the results from SAM scores. Tests are performed on hyperspectral images that have 360 spectral bands between 400 and 900 nm, collected by a specifically designed vehicular system for proximal image acquisition. The acquired images have eight classes, including three different pavement classes (good (5-year), medium (10-year), and poor (25-year)), yellow dye, white dye, soil, paving stone, and shadow. Several experiments are performed to evaluate the robustness of the followed methodology with limited learning data that include 5, 10, 25, and 50 samples per class, selected randomly from our independent spectral database. For a fair comparison, the individual ANN, SAM, support vector machine (SVM), and stacked auto-encoders (SAE) algorithms are also evaluated. The classification performances of individual ANN and SAM are significantly increased with their joint use, demonstrating a 1.2% to 21% classification accuracy improvement in relation to the training sample size. The study proves that the proposed approach is quite robust in cases wherein few training data are available, while SAE and standard ANN algorithms are more successful in cases wherein more learning data are present.

Impact of differences in speciation of organic compounds in wastewater from large WWTPs on technological parameters, economic efficiency and modelling of chemically assisted primary sedimentation process

The aim of the study was to supplement the information missing in the literature on the influence of organic compounds speciation (percentage of chemical oxygen demand (COD) fractions, readily biodegradable and non–biodegradable dissolved organic matter; slowly biodegradable and non‒biodegradable insoluble organic matter) in wastewater on chemically assisted primary sedimentation process and its potential impact on the energy and economic balance of the plant. The statistical modelling of the chemically assisted primary sedimentation process using artificial neural networks (ANN) was a novelty. It was also important to check the differences resulting from the use of coagulants, polyelectolites and their doses. For both large wastewater treatment plants (>500,000 people equivalent) located in the various regions of the country in Central Europe only ferric chloride had a positive balance. The addition of polyelectrolyte in the Wroclaw wastewater did not affect the chemically assisted primary sedimentation process but in the Bialystok wastewater it strengthened the process effect. In both plants only doses from 10 to 40 mgFe/dm3 are cost-effective. The results indicate that the addition of polyelectrolyte should be taken into account in this application and was correlated with the amount of colloidal COD fractions in wastewater. The developed ANN models reflected well the effectiveness of COD removal in both large WWTP while the sensitivity analysis of individual ANN models showed differences in the variables affecting the calculation process of both models, despite similar values of parameters characterizing wastewater in both wastewater treatment plants.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

An Extended Artificial Neural Network Assisted Hybrid Harris Hawks and Whale Optimizer to Find Optimal Solution for Engineering Design Problems

The algorithms that have been developed recently have decorous behavior to solve and find optimum solution to various optimization problems in search space. Withal such calculations stuck in issues nearby quest space for compelled engineering problems. In succession to achieve an optimal solution a hybrid algorithmic approach is proffered. Artificial Neural Network (ANN) is considered as better solution for the known outputs. A hybrid variant of applying ANN on Harris Hawks and Whale Optimization Algorithm (ANNHHOWOA) is proposed to achieve effective solution for engineering problems. The effectiveness of proposed algorithm is tested for various non-linear, non-convex and standard engineering problems and to approve consequences of proposed algorithm standard benchmarks and multidisciplinary design problems have been considered. The validation endorsed that the results shown by ANNHHOWOA showed much better results than individual ANN, HHO and WOA and its effectiveness on multidisciplinary engineering problems.

Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks

Due to the shortage of clear equations in the building codes that explain shear strength for fiber reinforced concrete (FRC) beams; there is a need to develop a numerical approach that can be used to predict shear behavior in FRC. The main objective of this research is to develop an artificial Neural Network (ANN) that can predict shear strength and simplify its use through developing a Graphic User Interface (GUI). Moreover, shear behavior in fiber reinforced concrete beams (FRCBs) is quantified by compressive strength of concrete, longitudinal steel, size effect, fiber's type, content and aspect ratio. The research methodology is based on collecting experimental results of technical investigations carried out to predict shear behavior in FRCBs. ANN aims at reducing the amount of computing time required in the numerous iterations involving structural analysis and experimental work. For this, two back-propagation neural networks have been experimented by MATLAB program; their types have been fitting (1st network) and pattern recognition (2nd network) which are used to classify failure of FRC beams into 6 categories. Through simulation study, the optimum architectures for the individual ANNs have been determined. The training algorithms used feed forward back propagation. The ANNs model has been assessed in comparison with exact values and deduces a good correlation with it. Finally, a software program is developed as an evaluation system for predicting resistance of FRC beams to shear forces, and to expect the failure pattern in order to avoid its occurrence.

Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties

For improved drought planning and response, there is an increasing need for highly predictive and stable drought prediction models. This paper presents the performance of both homogeneous and heterogeneous model ensembles in the satellite-based prediction of drought severity using artificial neural networks (ANN) and support vector regression (SVR). For each of the homogeneous and heterogeneous model ensembles, the study investigates the performance of three model ensembling approaches: (1) non-weighted linear averaging, (2) ranked weighted averaging, and (3) model stacking using artificial neural networks. Using the approach of “over-produce then select”, the study used 17 years of satellite data on 16 selected variables for predictive drought monitoring to build 244 individual ANN and SVR models from which 111 models were automatically selected for the building of the model ensembles. Model stacking is shown to realize models that are superior in performance in the prediction of future drought conditions as compared to the linear averaging and weighted averaging approaches. The best performance from the heterogeneous stacked model ensembles recorded an R2 of 0.94 in the prediction of future (1 month ahead) vegetation conditions on unseen test data (2016–2017) as compared to an R2 of 0.83 and R2 of 0.78 for ANN and SVR, respectively, in the traditional approach of selection of the best (champion) model. We conclude that despite the computational resource intensiveness of the model ensembling approach, the returns in terms of model performance for drought prediction are worth the investment, especially in the context of the continued exponential increase in computational power and the potential benefits of improved forecasting for vulnerable populations.