Prediction of carbon dioxide (CO2) emissions from agricultural soil is vital for efficient and strategic mitigating practices and achieving climate smart agriculture. This study aimed to evaluate the ability of two machine learning algorithms [gradient boosting regression (GBR), support vector regression (SVR)], and two deep learning algorithms [feedforward neural network (FNN) and convolutional neural network (CNN)] in predicting CO2 emissions from Maize fields in two agroclimatic regions i.e., continental (Debrecen-Hungary), and semi-arid (Karaj-Iran). This research developed three scenarios for predicting CO2. Each scenario is developed by a combination between input variables [i.e., soil temperature (Δ), soil moisture (θ), date of measurement (SD), soil management (SM)] [i.e., SC1: (SM + Δ + θ), SC2: (SM + Δ), SC3: (SM + θ)]. Results showed that the average CO2 emission from Debrecen was 138.78 ± 72.04 ppm (n = 36), while the average from Karaj was 478.98 ± 174.22 ppm (n = 36). Performance evaluation results of train set revealed that high prediction accuracy is achieved by GBR in SC1 with the highest R² = 0.8778, and lowest root mean squared error (RMSE) = 72.05, followed by GBR in SC3. Overall, the performance MDLM is ranked as GBR > FNN > CNN > SVR. In testing phase, the highest prediction accuracy was achieved by FNN in SC1 with R² = 0.918, and RMSE = 67.75, followed by FNN in SC3, and GBR in SC1 (R² = 0.887, RMSE = 79.881). The performance of MDLM ranked as FNN > GRB > CNN > SVR. The findings of the research provide insights into agricultural management strategies, enabling stakeholders to work towards a more sustainable and climate-resilient future in agriculture.

Water availability for agricultural practices is dynamically influenced by climatic variables, particularly droughts. Consequently, the assessment of drought events is directly related to the strategic water management in the agricultural sector. The application of machine learning (ML) algorithms in different scenarios of climatic variables is a new approach that needs to be evaluated. In this context, the current research aims to forecast short-term drought i.e., SPI-3 from different climatic predictors under historical (1901–2020) and future (2021–2100) climatic scenarios employing machine learning (bagging (BG), random forest (RF), decision table (DT), and M5P) algorithms in Hungary, Central Europe. Three meteorological stations namely, Budapest (BD) (central Hungary), Szeged (SZ) (east south Hungary), and Szombathely (SzO) (west Hungary) were selected to forecast short-term agriculture drought i.e., Standardized Precipitation Index (SPI-3) in the long run. For this purpose, the ensemble means of three global circulation models GCMs from CMIP6 are being used to get the projected (2021–2100) time series of climatic indicators (i.e., rainfall R, mean temperature T, maximum temperature Tmax, and minimum temperature Tmin under two scenarios of socioeconomic pathways (SSP2-4.5 and SSP4-6.0). The results of this study revealed more severe to extreme drought events in past decades, which are projected to increase in the near future (2021–2040). Man-Kendall test (Tau) along with Sen's slope (SS) also revealed an increasing trend of SPI-3 drought in the historical period with Tau = −0.2, SS = −0.05, and near future with Tau = −0.12, SS = −0.09 in SSP2-4.5 and Tau = −0.1, SS = −0.08 in SSP4-6.0. Implementation of ML algorithms in three scenarios: SC1 (R + T + Tmax + Tmin), SC2 (R), and SC3 (R + T)) at the BD station revealed RF-SC3 with the lowest RMSE RFSC3-TR = 0.33, and the highest NSE RFSC3-TR = 0.89 performed best for forecasting SPI-3 on historical dataset. Hence, the best selected RF-SC3 was implemented on the remaining two stations (SZ and SzO) to forecast SPI-3 from 1901 to 2100 under SSP2-4.5 and SSP4-6.0. Interestingly, RF-SC3 forecasted the SPI-3 under SSP2-4.5, with the lowest RMSE = 0.34 and NSE = 0.88 at SZ and RMSE = 0.34 and NSE = 0.87 at SzO station for SSP2-4.5. Hence, our research findings recommend using SSP2-4.5, to provide more accurate drought predictions from R + T for future projections. This could foster a gradual shift towards sustainability and improve water management resources. However, concrete strategic plans are still needed to mitigate the negative impacts of the projected extreme drought events in 2028, 2030, 2031, and 2034. Finally, the validation of RF for short-term drought prediction on a large historical dataset makes it significant for use in other drought studies and facilitates decision making for future disaster management strategies.

The development of new techniques, such as machine learning (ML), can provide better insight into the processes and drivers of soil erosion and runoff. However, the performance of these techniques to assess soil erosion in agricultural landscapes is poorly understood. The aim of this study was to evaluate the performance of four machine learning algorithms, generalized linear model (GLM), Random Forest (RF), elastic net regression (EN) and multiple adaptive regression splines (MARS), in predicting soil erosion and runoff in Syria. Soil erosion and runoff were measured on three experimental plots (2.25 m × 1.50 m × 0.50 m, 0.10 m depth in the soil), combined with three different slopes and land use types: RS (8%, olive), SS (12%, citrus), KS (20%, pomegranate). Both erosion and runoff were determined after rainfall events of >10 mm between October 2019 and April 2020. Based on 24 effective rainfall events, the average soil erosion was 0.18 ± 0.14 kg m⁻² per event in KS, 0.14 ± 0.11 kg m⁻² per event in SS, and 0.12 ± 0.10 kg/m² per event in RS. Regression analysis indicated strong relationship between the rainfalls and the runoff, the highest connection was recorded in the KS plot (r² = 0.85; p < 0.05 n = 24). The analysis of covariance indicated that only the runoff had a significant impact on soil erosion (p = 0.02) with a medium effect (ε²p = 0.26). However, the impacts of rainfall events and slope categories on soil erosion were limited (ε²p < 0.01) and not significant (p > 0.05). ML techniques were usually efficient in the prediction, the RF and MARS models were the most accurate: RF had the strongest correlation with the measured values (r = 0.85) with a low estimation error (0.06 kg m⁻²), but MARS's standard deviation (SD) was closer to the recorded values' SD. GLM and EN were the weakest predictor models. Modeled values of the slightest slope (8%) had the worst accuracies, and the predictions of the 12% slope were the best in all models. This study provides important insights into the usefulness of machine learning techniques and algorithms in predicting the rate of soil erosion and runoff in agricultural dominated landscapes. We highlighted that the RF and MARS algorithms were better predictors of soil erosion and runoff in the coastal region of Syria.

The lack of sufficient precipitation data has been a common problem for water resource planning in many arid and semi-arid regions with sparse and limited weather monitoring networks. Satellite-based precipitation products are often used in these regions to improve data availability. This research presents the first validation study in Syria for Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates using in-situ precipitation data. The validation was performed using accuracy and categorical statistics in the semi-arid Barada Basin, Syria, between 2000 and 2020. Multiple temporal scales (daily, pentad, monthly, seasonally, and annual) were utilized to investigate the accuracy of CHIRPS estimates. The CHIRPS results indicated advantages and disadvantages. The main promising result was achieved at the seasonal scale. Implementing CHIRPS for seasonal drought was proven to be suitable for the Barada Basin. Low bias (PBwinter = 2.1%, PBwet season = 12.7%), high correlation (rwet season = 0.79), and small error (ME = 4.25 mm/winter) support the implementation of CHIRPS in winter and wet seasons for seasonal drought monitoring. However, it was observed that CHIRPS exhibited poor performance (inland pentads) in reproducing precipitation amounts at finer temporal scales (pentad and daily). Underestimation of precipitation event amounts was evident in all accuracy statistics results, and the magnitude of error was higher with more intense events. CHIRPS results better corresponded in wet months than dry months. Additionally, the results showed that CHIRPS had poor detection skill in drylands; on average, only 20% of all in-situ precipitation events were correctly detected by CHIRPS with no effect of topography found on detection skill performance. This research could be valuable for decision-makers in dryland regions (as well as the Barada Basin) for water resource planning and drought early warning systems using CHIRPS.

The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) (°C), daily maximum mean temperature (Td-max) (°C), and daily minimum mean temperature (Td-min) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN–MLP and ANN–RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.

Drought is a natural hazard which affects ecosystems in the eastern Mediterranean. However, limited historical data for drought monitoring and forecasting are available in the eastern Mediterranean. Thus, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this context, the main goals of this research were to capture agricultural and hydrological drought trends by using the Standardized Precipitation Index (SPI) and to assess the applicability of four ML algorithms (bagging (BG), random subspace (RSS), random tree (RT), and random forest (RF)) in predicting drought events in the eastern Mediterranean based on SPI-3 and SPI-12. The results reveal that hydrological drought (SPI-12, −24) was more severe over the study area, where most stations showed a significant (p < 0.05) negative trend. The accuracy of ML algorithms in drought prediction varied in relation to the implementation stage. In the training stage, RT outperformed the other algorithms (Root mean square error (RMSE) = 0.3, Correlation Coefficient (r) = 0.97); the performance of the algorithms can be ranked as follows: RT > RF > BG > RSS for both SPI-3 and SPI-12. In the testing stage, both the BG and RF algorithms had the highest correlation r (observed vs. predicted) (0.58–0.64) and lowest RMSE (0.68–0.88). In contrast, the lowest correlation r (observed vs. predicted) (0.3–0.41) and highest RMSE (0.94–1.10) was calculated for the RT algorithm. However, BG was more dynamic in drought capturing, with the lowest RMSE and highest correlation. In the validation stage, the BG performance was satisfactory (RMSE = 0.62–0.83, r = 0.58–0.79). The output of this research will help decision-makers with drought mitigation plans by using the new four machine learning algorithms.

In watershed modeling research, it is practical to subdivide a watershed into smaller units or sub-watersheds for modeling purposes. The ability of a model to simulate the watershed system depends on how well watershed processes are represented by the model and how well the watershed system is described by model input. This study is conducted to evaluate the impact of watershed subdivision and different weather input datasets on streamflow simulations using the soil and water assessment tool model. For this purpose, Cuhai-Bakonyer watershed was chosen as a study area. Two climate databases and four subdivision variations levels were evaluated. The model streamflow predictions slightly effected by subdivision impact. The climate datasets showed significant differences in streamflow predictions.

In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CAR-BATCLIM database (1961–2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as −3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.