Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection.

The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

Effective microorganism water treatment method for rapid eutrophic reservoir restoration.

Since reservoirs perform many important functions, they are exposed to various types of unfavorable phenomena, e.g., eutrophication which leads to a rapid growth of algae (blooms) that degrade water quality. One of the solutions to combat phytoplankton blooms are effective microorganisms (EM). The study aims to evaluate the potential of EM in improving the water quality of the Turawa reservoir on the Mala Panew River in Poland. It is one of the first studies providing insights into the effectiveness of using EM in the bioremediation of water in a eutrophic reservoir. Samples for the study were collected in 2019-2021. The analysis showed that EM could be one of the most effective methods for cleaning water from unfavorable microorganisms (HBN22, HBN36, CBN, FCBN, FEN) - after the application of EM, a reduction in their concentration was observed (from 46.44 to 58.38% on average). The duration of their effect ranged from 17.6 to 34.1 days. The application of EM improved the trophic status of the Turawa reservoir, expressed by the Carlson index, by 7.78%. As shown in the literature review, the use of other methods of water purification (e.g., constructed wetlands, floating beds, or intermittent aeration) leads to an increase in the effectiveness and a prolongation of the duration of the EM action. The findings of the study might serve as a guide for the restoration of eutrophic reservoirs by supporting sustainable management of water resources. Nevertheless, further research should be conducted on the effectiveness of EM and their application in the remediation of eutrophic water reservoirs.

Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation.

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017-2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h-1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash-Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h-1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm3 cm-3 just after irrigation in 2 L h-1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm3 cm-3 were in 4 L h-1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm3 cm-3 were in 8 L h-1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system.

Eco-hydrological modeling of soil wetting pattern dimensions under drip irrigation systems.

Reliable information on the horizontal and vertical dimensions of the wetted soil beneath a point source is critical for designing accurate, cost-effective, and efficient surface and subsurface drip irrigation systems. Several factors, including soil properties, initial soil conditions, dripper flow rate, number of drippers, spacing between drippers, irrigation management, plant root characteristics, and evapotranspiration, influence the dimensions and shape of wetting patterns. The objective of this study was to briefly review previous studies, collect the analytical, numerical, and empirical models developed, and evaluate the effectiveness of the most common empirical method for predicting the dimensions of soil wetted around drippers using measured data from field surveys. With this review study, we aim to promote a better understanding of soil water dynamics under point-source drip irrigation systems, help improve soil water dynamics under point-source drip irrigation systems, and identify issues that should be better addressed in future modeling efforts. A drip irrigation system was configured with three different emitters with different capacities (2, 4, and 8 l h-1) in the point source to determine the soil wetting front under the point source. The five most selected empirical equations (Al-Ogaidi, Malek and Peters, Amin and Ekhmaj, Li and Schwartzman and Zur) were statistically analyzed to test the efficiency in sandy loam soil. According to the results of the field investigation, statistical comparisons of the empirical models with the field investigation data were performed using the mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe model efficiency (CE), and coefficients of determination (R2). The advanced simulation of the wetting front was used based on the best accuracy of the selected empirical model. In general, the Li model (MAE, RMSE, EF, and R2 were 0.698 cm, 0.894 cm, 0.970 cm2 cm-2, and 0.970, respectively, for the wetted soil width and 1.800 cm, 1.974 cm, 0.927 cm2 cm-2, and 0.986, for the vertical advance) proved to be the best after statistical analysis with field data.

Soil erosion control from trash residues at varying land slopes under simulated rainfall conditions.

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%.

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test.

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0.994 and minimum value of RMSE = 0.109, MAE = 0.041, MBE = -0.010 and RE = -0.1%; combination 2; NSE = 0.941, d = 0.984, KGE = 0. 923, PCC(r) = 0. 973, and R2 = 0. 947 and minimum value of RMSE = 0. 331, MAE = 0.143, MBE = -0.089 and RE = -0.9%) performed superior to the GRG, LR, LR-RSS, LR-SVM, and LR-M5P models in all input combinations during the testing period. It was also noticed that the performance of the alone LR and its hybrid models (i.e., LR-RSS, LR-REPTree, LR-SVM, and LR-M5P) was better than the conventional stage-discharge rating curve, including the GRG method.

A residue management machine for chopping paddy residues in combine harvested paddy field.

Nowadays, Combine Harvesters are the most commonly used device for harvesting crops; as a result, a large amount of plant material and crop residue is concentrated into a narrow band of plant material that exits the combine, challenging the residue management task. This paper aims to develop a crop residue management machine that can chop paddy residues and mix them with the soil of the combined harvested paddy field. For this purpose, two important units are attached to the developed machine: the chopping and incorporation units. The tractor operates this machine as the main source, with a power range of about 55.95 kW. The four independent parameters selected for the study were rotary speed (R1 = 900 & R2 = 1100 rpm), forward speed (F1 = 2.1 & F2 = 3.0 Kmph), horizontal adjustment (H1 = 550 & H2 = 650 mm), and vertical adjustment (V1 = 100 & V2 = 200 mm) between the straw chopper shaft and rotavator shaft and its effect was found on incorporation efficiency, shredding efficiency, and trash size reduction of chopped paddy residues. The incorporation of residue and shredding efficiency was highest at V1H2F1R2 (95.31%) and V1H2F1R2 (61.92%) arrangements. The trash reduction of chopped paddy residue was recorded maximum at V1H2F2R2 (40.58%). Therefore, this study concludes that the developed residue management machine with some modifications in power transmission can be suggested to the farmers to overcome the paddy residue issue in combined harvested paddy fields.

Assessment of future water demand and supply using WEAP model in Dhasan River Basin, Madhya Pradesh, India.

Understanding the available resources and the needs of those who use them is necessary for the evaluation and allocation of water resources. The main sectors utilizing the basin water resources are agriculture, drinking water, animal husbandry, and industries, and the efficient and rational management of water resources to be distributed among those different sectors of activity is vital. This study attempts to develop an integrated water resource management system for the Dhasan River Basin (DRB) by employing a scenario analysis approach in conjunction with Water Evaluation and Planning Model (WEAP) to analyze trends in water use and anticipated demand between 2015 and 2050, simulating five possible scenarios (I, II, III, IV, and V) as for external driving factors. For the WEAP modeling framework, 2015 was chosen as a current (base) year for which all available information and input data were given to the model and the future demand situation was analyzed for the period 2016-2050 (forecasting period). From the findings, it was observed that for the forecasting period, total water demand, unmet demand, and streamflow were 185.29 Bm3, 117.35 Bm3, and 58.26 Bm3, respectively, in the case of scenario I; 232.34 Bm3, 162.17 Bm3, and 59.87 Bm3 in case of scenario II; 139.40 Bm3, 84.37 Bm3, and 58.15 Bm3 in case of scenario III; 186.15 Bm3, 118.76 Bm3, and 56.98 Bm3 in case of scenario IV; and 181.89 Bm3, 96.87 Bm3, and 53.11 Bm3 in case of scenario V. Results of the study indicated that by 2050, increasing population growth, industrial development, and an increase in the agricultural area will rise the water demand dramatically, posing threats to the environment and humans. Therefore, implementing improved irrigation technologies, advancing agricultural practices on farms, and constructing water conservation and retaining structures could significantly reduce the unmet demands and shortfalls in DRB. Overall findings reveal that the pressure on the Dhasan water resources would increase in the future, and thus several suggestions have been provided to assist decision-makers in sustainable planning and management of water resources to meet future demands.

Detection of Water Spread Area Changes in Eutrophic Lake Using Landsat Data.

Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann-Kendall test was used. San's slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake's water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions.