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).

Streamflow forecasting based on past records is an important issue in both hydrologic engineering and hydropower reservoir management. In the study, three artificial Neural Network (NN) models, namely NN with well-known multi-layer perceptron (MLPNN), NN with principal component analyses (PCA-NN), and NN with time lagged recurrent (TLR-NN), were used to 1, 3, 5, 7, and 14 ahead of daily streamflow forecast. Daily flow discharges of Haldizen River, located in the Eastern Black Sea Region, Turkey the time period of 1998-2009 was used to forecast discharges. Backpropagation (BP), Conjugate Gradient (CG), and Levenberg- Marquardt (LM) were applied to the models as training algorithm. The result demonstrated that, firstly, the forecast ability of CG algorithm much better than BP and LM algorithms in the models; secondly, the best performance was obtained by PCA-NN and MLP-NN for short time (1, 3, and 5 day-ahead) forecast and TLR-NN for long time (7 and 14 day-ahead) forecast.

The doubt in the calculation algorithm of the standardized precipitation index (SPI), which is widely preferred in the evaluation and monitoring of drought, still remains up-to-date because its calculation process is performed in the form of standardization or normalization with a default probability distribution. Therefore, the success of this index is directly affected by the choice of the probability distribution model. This study is based on the effect of three different parameter estimation methods on the calculation process, as well as the comparison of the SPI results calculated based on the default Gamma distribution and the distribution with the best ability to represent the 3-and 12-month consecutive summed rainfall data among the 15 candidate distributions namely Gamma (GAM), Generalized Extreme Value (GEV), Pearson Type III (P III), Log Pearson Type III (LP III), two-parameter Lognormal (LN2), three-parameter Lognormal (LN3), Generalized Logistic (GLOG), Extreme Value Type I (EVI), Generalized Pareto (GPAR), Weilbul (W), Normal (N), Exponential (EXP), Logistic (LOG), four-parameter Wakeby (WK4), and five-parameter Wakeby (WK5) distributions. Approximately 68.4% and 18.4% of the 3-month data considered had the best fit to the Weibull and Pearson III distribution, while approximately 24% and 18% of the 12-month data had the best fit to the Weibull and Logistic distribution. On the other hand, it was found that the default Gamma distribution calculated the extreme drought categories significantly more than the best-fit distribution model. In terms of parameter estimation methods, L-moments for 3-month series and maximum likelihood approaches for 12-month series were most dominant.

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.