Landslides are among the major natural disasters that cause widespread destruction, significant loss of life, and substantial economic damage. This study ensembles the Random Forest (RFT) model with three bivariate models, namely Frequency Ratio (FRO), Evidential Belief Function (EBFN), and Index of Entropy (IOEY) in a landslide prone region that is located in the highest parts of the watershed of two large rivers of Iran. Twelve landslide (LSE) influencing factors were chosen after a VIF and TOL test. Additionally, 80 LSE inventories were prepared from multiple sources and field studies and randomly classified into training and validation (70:30 ratio) for model building and validation, respectively. The weightage of conditioning domains was derived using the FRO, EBFN, and IOEY. The Wij Values by the IOEY model showed that lithology has maximum weightage, followed by LULC, and TWI has minimum weightage while other factors played an intermediate role. Geographically, the lower part of the basin is characterized by very high and high susceptibility, whereas the northern part has very low and low susceptibility. The IOEY-RFT model indicates that 20.48% of the total basin area displays very high susceptibility, 20.51% is characterized by high susceptibility, 7.91% by moderate susceptibility, 29.32% by low susceptibility, and 21.78% is covered by very low susceptibility. Model performance on the independent validation set is reported using the Area Under the ROC Curve (AUC) and True Skill Statistic (TSS). The IOEY-RF ensemble achieved an AUC of 0.915 and a TSS of 0.894, while FRO-RF and EBFN-RF achieved AUCs of 0.892 and 0.890, respectively, and TSSs of 0.871 and 0.864, respectively. All three ensemble models outperformed their single method counterparts (FRO, EBFN, IOEY) on validation metrics. Our study provides a new approach to evaluate landslides in specific geographic and geoenvironmental settings.

Following publication, concerns were raised regarding the relevance of a few references in this publication [...]

Spatial modelling and susceptibility mapping of soil erosion are crucial for planning effective control strategies and land-use changes to mitigate future degradation and aid soil conservation. In this study we employed an ensemble statistical data-driven based prediction of gully erosion (GUE) susceptibility in Central Iran using the Weight of Evidence (WOE) method combined with four models: Stochastic Gradient Descent (SGD), Entropy (ENT), Multi-layer Perceptron (MLP), and Rotation Forest (RF). We used, 424 gully erosion locations, with 292 samples used to train the models and the rest for validation. Sixteen conditioning factors were considered in predicting gully erosion susceptibility zones. For accuracy assessment, the ROC curve was used, and the results show that the RF-WOE surpassed all other ensemble models with an accuracy of 93 %. This was followed by the SGD-WOE model at 92.6 %. Conversely, WOE model alone had the lowest accuracy at 89 %. Meanwhile, the MLP-WOE and ENT-WOE models showed accuracies of and 92.0 %, respectively. According to the RF-WOE model, about 40 % and 47.17 % land was classified under high and very high ceptible zone respectively that are dominantly located in the north, northeast, and southwest of the study region. Furthermore, analysis using the entropy model indicated that geomorphology, with a value of 0.684, was the highest contributing factor to gully erosion ceptibility, followed by distance to streams at 0.449. Conversely, soil depth was the least contributing factor. In conclusion, study demonstrates that machine learning-based ensemble models, particularly RF-WOE, effectively identify gully erosion susceptibility zones high accuracy, providing a valuable tool. This finding is significant for land managers and policymakers aiming to implement effective soil conservation measures invulnerable regions. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and technologies.

The Kosi Megafan, located in the Himalayan foreland basin, is highly susceptible to devastating floods, posing significant threats to lives and livelihoods. Accurate flood susceptibility mapping is crucial for effective flood risk management in this dynamic environment. This study evaluates and optimizes five advanced machine learning algorithms - Random Subspace, J48, Maximum Entropy (MaxEnt), Artificial Neural Network (ANN-MLP), and Biogeography-Based Optimization- for flood susceptibility zonation within the Kosi Megafan. A comprehensive dataset incorporating 19 conditioning factors, derived from ALOS PALSAR DEM, Sentinel-2A, Landsat 5 TM, ENVISAT-1 ASAR (ENVISAT-1 Advanced Synthetic Aperture Radar), and other ancillary data sources, was used to train and validate the models. Model performance was assessed using a suite of metrics, including accuracy, true skill statistics (TSS), sensitivity, specificity, Kappa, AUC, and the Seed Cell Area Index. Notably, the ANN-MLP model demonstrated exceptional performance on the validation dataset, achieving an accuracy of 0.982, TSS of 0.964, and Kappa of 0.964, outperforming the other models. MaxEnt also exhibited strong performance, confirming its robustness in environmental modeling. The analysis of variable importance revealed that normalized difference vegetation index (NDVI), altitude, distance to road, rainfall, and distance to river were the most influential factors governing flood susceptibility in the region. The generated flood susceptibility maps, particularly those derived from the ANN-MLP and MaxEnt models, provide valuable tools for identifying high-risk areas and informing flood mitigation strategies. This study highlights the potential of advanced machine learning techniques, especially ANN-MLP, in significantly improving the accuracy and reliability of flood susceptibility assessments in complex and dynamic environments like the Kosi Megafan, paving the way for more effective flood risk management and disaster preparedness.

Gully erosion is one of the major global environmental threats that frequently affects semi-humid to arid Mediterranean regions and contributes to a wide range of ecological problems. Recognizing vulnerable areas to gully erosion and creating a comprehensive gully erosion susceptibility map (GESM) can assist in the lessening of land degradation and damage to numerous infrastructures. The primary goal of this research is to build a random subspace-based function tree (RSFT), i.e., an ensemble model, and compare it with other standard models such as Fisher's linear discriminant analysis (FLDA), Nave Bayes tree (NBTree), J48 Decision Tree, and random forest (RF) models in order to identify which model generates the most accurate outcomes. Overall, a total number of 489 gully sites were utilised for modelling and validation purpose, with 377 (70 %) used for modelling and 112 (30 %) used for validation. Fourteen salient gully erosion conditioning factors (GECFs) were implemented for constructing the GESMs. The efficacy and significance of several GECFs were assessed through the random forest, or RF, model for gully erosion modelling. Using the GES maps, we computed the success rate curve (SRC) and prediction rate curve (PRC), as well as their areas under the curves (AUC). The AUC (SRC, PRC) scores for the RSFT model were 0.906 and 0.916, consequently, while the outcomes for the RF, NBTree, FLDA, and J48 models were 0.875 and 0.869, 0.861 and 0.859, 0.792 and 0.816, and 0.779 and 0.811. AUC findings indicated that the RSFT model delivered the most precise predictions, trailed by the RF, NBTree, FLDA, and J48 models. In terms of RMSE, each of the models performed adequately; however, RSFT exhibits the lowest RMSE values of all models, with 0.31 (training dataset) and 0.29 (validation dataset), which shows that RSFT is substantially more accurate than other models in forecasting gully erosionThus, the results of this research can be used by local managers and planners for environmental management. The results from our study suggests that all of the GESM models have high efficiency, and can be employed to formulate adequate measures for safeguarding of soil and water. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Tropical monsoon countries like Bangladesh have experienced erratic spatiotemporal rainfall distribution, heavy rainfall, and extensive erosion in recent decades. The erosive nature of the soil in the country poses a serious ecological problem. However, there is a lack of studies on the spatiotemporal distribution of rainfall erosivity and precipitation concentration trends in Bangladesh. This study intends to investigate the Rainfall erosivity over the past three decades in Bangladesh. Using the Precipitation Concentration Index (PCI) and the Modified Fournier Index (MFI), this study attempted to demonstrate precipitation concentration and erosivity distribution during 1991–2020. The PCI and MFI indices were calculated using monthly precipitation records from 30 observatories nationwide. PCI values ranged between 15.43

Floods are among the most devastating natural disasters, causing widespread loss of life, infrastructure damage, and long-term socio-economic disruption. Accurate flood susceptibility assessment is therefore vital for effective disaster risk reduction and environmental management. The Nekaroud watershed in Iran is particularly flood-prone due to its complex terrain and dense hydrological networks. This study employs Radial Basis Function Neural Network (RBFN)-based ensemble models to map and predict flood susceptibility in this challenging environment. A dataset comprising 133 recorded flood events was compiled, with 70

Landslides pose a serious threat, especially in mountainous regions, where they can severely impact human life, property and economic activities. This hazard has been particularly challenging in the Himalayan region, particularly in the Sikkim Himalayas. In this study, hybrid models are used to generate landslide susceptibility maps (LSMs) in the South Sikkim Himalaya region of India that could significantly help in reducing the risk. To construct an accurate susceptibility map, a detailed 163 landslide inventories were created using historical data, Google Earth and field investigations. A Random Forest (RF) model was employed to identify relevant factors that revealed 15 significant variables, out of which, distance from road and rainfall emerged as the primary drivers of landslide risk. The Stacking Ensemble model combines three classic machine learning models, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Gradient Boosting Decision Trees (GBDTs) to avoid overfitting and improve generalisation. For modelling, landslide data were randomly divided into training (70%) and validation (30%) subsets. The models were evaluated using the receiver operating characteristic (ROC) curve and statistical metrics. The results showed that the ANN had the highest individual prediction performance with an area under curve (AUC) value of 0.92, followed by GBDT (0.89), SVM (0.83) and the Stacking method (0.89). However, the Stacking model performed best in generalisation and class-specific accuracy, particularly in very high (0.54%) compared to other models. The results of this study highlight the Stacking model's promise as a reliable tool for landslide susceptibility mapping by showing that it performs better than alternative methods in precisely identifying landslide-prone locations. The combination of many machine learning methods inside a hybrid framework markedly improves the dependability and accuracy of these maps. The utilisation of hybrid models enhances understanding of landslide dynamics and establishes a scientific foundation for formulating efficient risk reduction methods. This improvement offers critical insights for policy-making focused on reducing landslide risks and supports long-term regional growth and sustainability, especially in geologically vulnerable and mountainous areas.

The frequency and occurrence of earth fissures threaten human settlements, infrastructure, and agricultural lands in many parts of the globe. This natural hazard has particularly posed great challenge in the North, Central, and Eastern parts of the Najaf Abad Basin of Iran. This paper presents a comparative analysis of machine learning algorithms for earth fissure susceptibility assessment, and involves the creation of an earth fissure database by selecting and validating 200 fissure locations. To improve the accuracy of the fissure locations, various geographic data sources, such as Google Earth images, field surveys, and GPS measurements, were employed. The locations were divided into training and validation sets based on sixteen conditioning factors for finding the earth fissure susceptible zones using EBF (Evidential belief function), EBF - Multilayer Perceptron (MLP), EBF - Function Tree (FT), EBF - Alternating decision tree (ADtree), EBF - Adaptive Boosting (AdaBoost), EBF - Random SubSpace (RSS), EBF - Naive Bayes Tree (NBTree) models. The results show that earth fissures occur in greater concentrations where groundwater levels are lowest, and their prevalence is closely associated to both agricultural activities and the presence of clay, clayey sand, and silty clay soils. Susceptibility analysis results revealed that 15-21 % agricultural dependent population falls under the very high susceptibility risk zone in and around the major cities of the Najaf Abad region. The receiver operating characteristic (ROC) curve analysis showed that the EBF-RSS algorithm has higher accuracy (>99 %) compared to other algorithms (>85 %) in training and validation phases for earth fissure susceptibility prediction. Our findings emphasise the need to prioritise these susceptible areas for sustainable land and water management strategies and implement effective measures to mitigate the impact of earth fissures. Our study also advances our knowledge on geospatial analysis and earth fissures-related hazard assessment by applying five machine learning algorithms that shows spatial similarity in susceptibility categories with splendid accuracy performance. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Agricultural droughts are a periodic phenomenon in many regions of the word. Reducing damages to crops from drought events requires increasing the precision of agricultural drought mapping and making forecasts. However, there are very few studies that employ cutting-edge ensemble learning methods to estimate the risk of agricultural drought. In order to strengthen predictions of agricultural drought risk in a systematically explicit manner, we propose and evaluate ensemble models grounded on the credal decision tree (CDT) with Decorate (CDT-Decorate) supervised learning approaches as a case study in Iran. In the Esfahan Province of Iran, a thorough evaluation of the possibility of agricultural drought was conducted. This assessment coupled the five risk components-vulnerability, hazard, exposure, and mitigation-with the parameters that were suitable. Eighteen drought conditioning factors were identified and used to build both the training and validation datasets. A number of evaluation measures that showed the ensemble model's capacity to explain the underlying spatial pattern of agricultural drought events within the research area and forecast the likelihood of future drought phenomena that were used to validate the models. Area Under the Receiver Operating Characteristic Curve (AUCROC) showed that the ensemble CDT-Decorate model was better than the CDT model (AUCROC = 0.755) because it had an AUCROC value of 0.962. When assessing the risk of agricultural drought, the most important elements are population density, land usage, land cover, and distance to the river. The center region, with its intermediate risk (17 %), has a significant disruption of human agricultural activities; the southern region, with its very high risk (16 %), should receive the greatest attention due to its high susceptibility, significant hazardousness, and limited mitigation capability. An analysis of the models' e performances revealed that the ensemble model offered a trustworthy assessment of the risks associated with agricultural drought, and the risk maps it produced are suitable for drought mitigation techniques in the agricultural sector and could be applied in other drought-prone areas.

Landslides are a prevalent geologic phenomenon that substantially threatens human life and infrastructure, resulting in considerable loss and destruction. The practice of landslide susceptibility mapping is crucial for the mitigation of risks connected with this natural disaster. This work aims at investigating the influence of varying sample sizes on the precision of landslide susceptibility modelling using a case study conducted in the Alamout basin, Iran. The researchers used a machine learning methodology based on tree algorithms to construct a model for predicting the likelihood of landslides. Additionally, they adopted a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The integration of the naive Bayes tree (NBTree), random forest (RF), logistic model tree (LMT) and J48 algorithms was performed. The modelling process included using 20 predictive parameters across four distinct scenarios. Four models, labelled S1, S2, S3 and S4, were used in this study. These models utilized 25%, 50%, 75% and 100% of the available inventory data. The research presented in this study is distinguished by using a tree‐based methodology for landslide susceptibility modelling and incorporating a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The findings indicated that the augmentation of the sample size improved the precision of the models. The efficacy of using a multi‐scenario strategy in enhancing the dependability of the model is also underscored. Among the 20 input elements used in the modelling process, it was seen that slope angle accounted for the highest relative significance, constituting 25.60% of the overall influence. Following more closely, distance to fault contributed significantly, with a relative importance of 23.40%. Additionally, rainfall and elevation exhibited notable contributions, with relative volumes of 7.91% and 5.50%, respectively. All four landslide models showed adequate learning and forecasting ability throughout the training and testing phases. During the testing phase, the true skill score (TSS) values exhibited a range of 0.631–0.804, while the area under the receiver operating characteristic curve values showed a range of 0.745–0.921. The susceptibility maps indicated that a significant portion of the region exhibits moderate to very high susceptibility zones, with the northern and eastern sectors displaying greater landslide values than the western region. The model's performance showed improvement from S1 to S4 in both the training and testing phases. The performance of the models exhibited the following trend: in scenario 1, the RF model outperformed the J48, LMT and NBTree models; in scenario 2, the RF model surpassed the NBTree and LMT models, while being on par with the J48 model; in scenarios 3 and 4, the RF model showed superior performance compared to the NBTree, J48 and LMT models. Therefore, the RF model proved to be the most effective among the models evaluated. The findings derived from this research have the potential to serve as valuable references for the purposes of land‐use planning and catastrophe risk management.

Technological alliances have become a popular strategy to cope with competitive pressures, short product life cycles, high research and development (R&D) costs and entry barriers. Firms gain access to complementary technologies, insulate from environmental uncertainty, increase knowledge base, access new markets and preserve leadership by using strategic alliances. In this regard, this study aims to establish a framework for R&D strategic alliance partner selection by utilizing a hybrid multi-criteria decision-making (MCDM) approach. In this way, at first, related R&D strategic alliance partner selection criteria are collected from existing literature, and then by using the best-worst method (BWM), which is a novel MCDM method, the weights of these criteria are calculated. After that by using the COPRAS method, available partners for R&D strategic alliance, ranked, and the best ones introduced. The calculation procedure for the weighting and evaluation processes are proposed and validated by using an illustrative example of Iran’s aviation industry. The proposed approach also provides a relatively simple and well-suited decision-making tool for this type of strategic decision-making problem.

The development of earth fissures, which are linear fractures with openings or offsets on the land surface, can severely affect landforms, especially in urban areas, in the form of earthquakes causing major concern on human lives as well as damage to infrastructures. Thus, an early warning map for lands susceptible to earth fissures can better equip planners for formulating mitigation strategies. In this study, we focus on the Damghan Plain in Iran for preparation of earth fissure susceptible maps using several topographical, hydrological, geological and environmental conditioning factors. In order to train these conditioning factors and preparation of earth fissure susceptibility maps, 124‐earth fissure field‐based samples, for training and validation purposes, were used by random subspace (RS) model based on four other machine learning ensemble methods such as RS‐Naïve‐Bayes Tree (NBTree), RS‐alternating decision tree (ADTree), RS‐Fisher's Linear Discriminant Function (FLDA) and RS‐Logistic model tree (LMT). From the validation technique, the receiver operating characteristic (ROC) curve performance test demonstrates that the RS‐NBTree model was the best suited with area under curve (AUC) = 0.974 followed by RS‐ADTree (AUC = 0.966), RS‐LMT (AUC = 0.954), RS‐FLDA (AUC = 0.948) and RS (AUC = 0.923). The results from our study can be useful for environmental management and risk reduction.

In this study, the existence of anchoring bias-people's tendency to rely on, evaluate, and decide based on the first piece of information they receive-is examined in two multi-attribute decision-making (MADM) methods, simple multi-attribute rating technique (SMART), and Swing. Data were collected from university students for a transportation mode selection. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Despite their differences in anchoring bias, analytical approaches supported by empirical studies suggest that both methods (SMART and Swing) overweigh the less important attributes and underweigh the more important attributes. As such, we examined whether the best-worst method (BWM), which has two opposite anchors in its procedure (a possible promising anchoring debiasing strategy), could produce results that are less prone to anchoring bias. Our findings show that the BWM is indeed able to produce lower weights (compared to SMART and Swing) for the less important attributes and higher weights for the more important attributes. This study shows the vulnerability of MADM methods with a single anchor and supports the idea that MADM methods with multiple (opposite) anchors, like BWM, are less prone to anchoring bias.

Gully erosion poses a threat to the sustainability of cultivated regions worldwide, is environmentally problematic, and causes significant rates of soil erosion. Understanding the formation process and gully development requires mapping gully erosion. An innovative modeling approach was used in this work using a case study in northern Iran. 14 gully-erosion parameter measurements from 1042 gully erosion locations were combined to produce a geographic dataset. For the purposes of modeling and validation, four training data sets (each in the standard 70:30 ratio) that represented 100 %, 75 %, 50 %, and 25 % of the total database were employed. The four machine learning models that were used to assess the utility of the four training situations were Bloom Filter Trees, Random Forest, (RF), Kernel Logistic Regression (KLR), and ensemble of KLR-RF. The most important effective factors, as determined by the RF research, were distance from the stream (72.61), vertical distance of the channel network (49.34), distance from the road (48.81), and elevation (47.68). The findings were validated using the receiver operating characteristic. Based on our findings, the combination of KLR and RF is the most optimum combination for both improved accuracy (0.928) and predictive capability (0.911). Out of them, the KLRRF 50 % training scenario is the most optimal when compared to the other scenarios. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The increasing soil erosion (SE) and the associated problems for society, economy, and environment sparked a lot of interest in estimating and mapping SE at different basin scales. The estimation of SE exhibits that SE ranges from 10 to 50 t ha(-1 )yr(-1), with a mean SE of 20 t ha(-1) yr(-1). The very steep slopes account for 54.21% of total soil loss. The SRB areas where soil loss rates are >10 t ha(-1 )yr(-1) are considered the target areas which account for 27% of the study area and 96% of the soil loss). The high SY is concentrated only in the first-order basins located in a higher slope zone in the northern part of the river. Besides, basin morphometry (basin shape, relative relief) and anthropogenic activities (agricultural land) are retained in the PSLR model as significant factors contributing to SY in the entire river basin.

Landslides pose significant impact on human life and society such as loss of livelihood, destruction of infrastructure, and damage to natural resources around the world. Due to existing complications in conditional factors of landslide, mapping and predicting landslide occurrences with high accuracy needs more attention. In light of this, we aim to develop an ensemble landslide susceptibility model named as support vector regression–grasshopper optimization algorithm (SVR–GOA). This model is validated along with other landslide susceptibility models such as artificial neural network (ANN), boosted regression tree (BRT), and elastic net models. The present study carried out over the Kalaleh Basin in Iran, in which we selected 140 landslides with 16 conditional factors to construct a geographic database of the region. The multicollinearity analysis was done on the hazard conditioning factors using variance inflation factor and tolerance indices. Similarly, significance of these factors and their association with selected locations were identified through random forest method. The state of the art of the study is implementing SVR-GOA in landslide susceptibility mapping, including this model we use other landslide models such as ANN, BRT, and elastic net for validation and development using the area under the curve (AUC), kappa, and root mean squared error values. Our results show lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were found to be the most influential conditioning factors. We have also observed that, despite accurate prediction, SVR-GOA outperforms the others by showing the highest AUC values around AUC = 0.930 and others show ANN (AUC = 0.833), BRT (AUC = 0.822) and elastic net (AUC = 0.726) respectively. This innovative approach to landslide mapping using SVR-GOA ensembles would enhance the advancement of landslide research at multiple scales.

Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in environmental damages and socio-economic issues. To foresee and prevent the impact of land subsidence, it is necessary to develop accurate maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas and to reduce or prevent land subsidence. In this study, we used a new approach to improve decision stump classification (DSC) performance and combine it with machine learning algorithms (MLAs) of naïve Bayes tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT), and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70

Any renewable energy supply chain's (RESC) performance measurement system comprises complex interconnected indicators. Hence, this research aims to propose an integrated RESC performance measurement framework containing all the performance measurement criteria and their indicators. This study identified six critical performance criteria through a combined expert opinion and literature review. RESC performance criteria and indicators are prioritized herein using the neutrosophic enhanced best–worst method (NE-BWM) that considers decision-makers (DM) opinions’ confidence rating levels. The top five indicators, i.e., “supply chain management cost,” “energy quality to consumers,” “reverse logistics,” “product quality,” and “information sharing,” and three critical performance criteria, i.e., “quality,” “supply chain efficiency,” and “service to the customer,” were identified. Findings point to the need for proper coordination/collaboration among RESC partners. Information sharing is a critical component of improving supply chain coordination/collaboration. Experts’ subjective inputs in demography are a significant limitation of this study. The NE-BWM results proposed that the top 10 indicators provided 90

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO-HFS due to their consistently high accuracy (RMSE and R-2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.