These capabilities make them particularly effective for addressing challenges in complex data-rich environments [31; 32; 33]. Consequently, ML (machine learning) models have been effectively utilized in environmental and natural hazard research, including tasks like identifying areas prone to land subsidence, landslides, floods, and predicting droughs [30; 33]. The ANFIS algorithm, a Neuro-Fuzzy approach that utilizes an adaptive network for learning, has been widely recognized for its effectiveness in forecasting time series within water resources management, dam management, risk assessment, and hydrological modeling. Renowned for its robust capabilities, ANFIS offers significant potential in addressing complex and nonlinear challenges [33; 34; 35]. The primary strengths of this method include its ability to manage fuzziness, effectively handle data that are insufficient in time or space, and the neural network's capacity for dissecting complex relationships between dependent and independent variables [33]. Currently, machine learning and deep learning techniques are often combined with optimization techniques to make more robust predictions for various parameters, such as drought, streamflow, energy consumption, etc. Using optimization techniques allows for the more precise calibration of ML models, leading to improved learning and predictions that better match observed values. These techniques are employed worldwide in a variety of fields, including health and the environment (Figure 1). Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are frequently preferred optimization techniques [36], as illustrated in Figure 1. To expand the scope of optimization techniques beyond those commonly employed ones in the literature, this study incorporated the Artificial Bee Colony (ABC) and Grey Wolf Optimization (GWO) methods, thereby enabling a more comprehensive comparison [37; 38]. Researchers often favor SVM over other machine learning algorithms, such as ANFIS, when estimating hydrological parameters. Numerous modeling studies employing SVM in hydrology have concluded that it is superior compared to other methodologies in terms of effectiveness [39]. Furthermore, some research has unveiled that integrating optimization approaches within hybrid models can yield even better results [40; 41]. Apart from ANFIS, SVM is also employed with various optimization methods for drought prediction. Recently, [PERSON] et al. [42] evaluated the effectiveness of four algorithms, SVM with Response Surface Method (SVM-RSM), SVM with PSO (SVM-PSO), standalone SVM, and ANN, using multiple drought indices. They reported that the SVM-PSO model demonstrated superior accuracy and trend detection compared to others, while SVM-RSM exhibited the highest efficiency. [PERSON] et al. [41] developed hybrid models for the prediction of meteorological droughts using the Effective Drought Index (EDI) and compared the results obtained by combining SVM with PSO (SVM-PSO) and Harris Hawks Optimization (SVM-HHO). They found that the SVM-HHO model outperformed the SVM-PSO model in accurately reproducing the median value for Uttarakhand, India. SVM-PSO applications extend beyond drought prediction, as it is also used for estimating hydrological parameters such as precipitation. [PERSON] et al. [43] utilized SVM-PSO to predict precipitation in China and proposed it as an efficient method for predicting precipitation. In this study, we hybridized ANFIS with GA and PSO, which are frequently used optimization algorithms in drought forecasting. We also hybridized ABC and GWO optimizations, which are among the new optimization techniques that have recently started to be used in hydrology with ANFIS. To further expand the methodological repertoire, we also developed a hybrid model by combining SVM, a commonly employed and effective algorithm, with PSO. That is, this study combined the ANFIS model with each of  
Figure 1: Use of optimization techniques. four popular optimization algorithms GA, GWO, ABC, and PSO) to generate hybridized models.