A serious natural disaster that poses a threat to people and their living spaces is drought, which is difficult to notice at first and can quickly spread to wide areas through subtle progression. Numerous methods are being explored to identify, prevent, and mitigate drought, and distinct metrics have been developed. In order to contribute to the research on measures to be taken against drought, the Standard Precipitation Evaporation Index (SPEI), one of the drought indices that has been developed and accepted in recent years and includes a more comprehensive drought definition, was chosen in this study. Machine learning and deep learning algorithms, including support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and Gaussian process regression (GPR), were used to model the droughts in six regions of Norway: Bodø, Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Four distinct model architectures were employed for this goal, and as a novel approach, the models’ output was enhanced by using discrete wavelet decomposition/transformation (WT). The model outputs were evaluated using the correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) as performance evaluation criteria. When the findings were analyzed, the GPR model (W-GPR), which was acquired after WT, typically produced the best results. Furthermore, it was discovered that, out of all the recognized models, M04 had the most effective model structure. Consequently, the most successful outcomes were obtained with W-SVM-M04 for Bodø and W-GPR-M04 for Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Furthermore, W-GPR-M04 in the Oslo region had the best results across all regions (r: 0.9983, NSE: 0.9966 and RMSE:0.0539).

Drought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. Meteorological data, such as monthly precipitation, that could represent the region were obtained between 1955 and 2020 from the general directorate of state water works and the general directorate of meteorology. According to the findings, the M04 model, whose input structure was developed using SPI, various time steps, data delayed up to 5 months, and monthly precipitation data from the preceding month (time t − 1), produced the best results out of all the models examined using machine learning algorithms. Among machine learning algorithms, SVM has achieved the most successful results not only before applying WT but also after WT. The best results were obtained from M04, in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria. The results of this study demonstrated that, while successful results were obtained in all commonly used algorithms except for ANFIS-GWO, the best performance values obtained using SPI12 input data were achieved with ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, and RMSE: 0.3108.

Streamflow forecasts play an active role in hydrological planning and taking precautions against natural disasters. Streamflow prediction models are frequently used by scientists, especially in dam management, sustainable agriculture, flood control, and flood mitigation. Hence, streamflow prediction modeling was performed in this study, and six models were employed through four different machine learning (ML) algorithms, namely, the artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision tree (DT) that are well known in the literature, in order to predict the monthly streamflow of Çarşamba River, Türkiye. To further enhance model performance, wavelet transform (WT) was applied to ML algorithms. In this study, monthly average streamflow and precipitation data between 1974 and 2015 was used, and the minimum redundancy maximum relevance method (MRMR) and the cross-correlation method were performed to determine model input data. Results of this study revealed that RF had superiority over the other models before WT, followed by the SVM model. The SVM after WT (W-SVM), M04 (r: 0.9846, NSE: 0.9695, and RMSE: 0.3536) gave the most effective performance results, while the W-ANN model (r: 0.9797, NSE: 0.9588, and RMSE: 0.4108) showed the second best performance.

Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices is limited before the beginning of the drought. To overcome this shortcoming, drought forecasting and providing decision-makers with an early warning to mitigate the effects is an important research topic. This study aims to take on the forecasting of the droughts with its novelty on the spatial focus, Norway (Drammen, Hamar, and Lillehammer). We forecast the Effective Drought Index (EDI) across spatially diverse Norwegian regions without hydrological constraints. To achieve this, we have utilized precipitation data between 1980 and 2025 and trained our machine learning models, namely, Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGboost), Long-Short Term Memory network (LSTM), and Categorical Boosting Algorithm (Catboost). Moreover, the latent feature space is extended by wavelet transformation (WT). The innovative aspect of this study and its contribution to the literature is its novel application of the WT to some algorithms. Furthermore, unlike the literature, EDI was chosen as the drought index in this study, further increasing its innovative nature. Our results indicate that long short-term memory networks enhanced by wavelet transformation provide the best forecasts. Here, the best performance, LSTMW-M04, is achieved over Drammen ( r = 0.9765, NSE = 0.9510, KGE = 0.8641, PI = 0.3211, and RMSE = 0.2207). Although LSTM is already an innovative and successful algorithm, we have further improved the model performance. This result will help decision-makers in a future drought study with both the model input structure and the algorithm used.