Soil temperature estimation at different depths using machine learning paradigms based on meteorological data.

Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models.

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water's temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water.

Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions.

Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain.

Evaluation of management practices on agricultural nonpoint source pollution discharges into the rivers under climate change effects.

In recent years, agricultural non-point source pollution (ANPSP) has become the biggest threat to Aras River water quality by completing the Mughan irrigation and drainage network. Nutrient pollutants, including nitrate and phosphate, released into the river through drains have created a range of obstacles for locals living around the river. Agricultural activities are generally considered the largest source of non-point pollution.

Cost-effective management measures for coastal aquifers affected by saltwater intrusion and climate change.

Sustainable management of natural water resources and food security in the face of changing climate conditions is critical to the livelihood of coastal communities. Increasing inundation and saltwater intrusion (SWI) will likely adversely affect agricultural production and the associated beach access for tourism. This study uses an integrated surface-ground water model to introduce a new approach for retardation of SWI that consists of placing aquifer fill materials along the existing shoreline using Coastal Land Reclamation (CLR).

Environmental rethinking of wastewater drains to manage environmental pollution and alleviate water scarcity.

The conservation of water resources in developed countries has become an increasing concern. In integrated water resource management, water quality indicators are critical. The low groundwater quality quantitates mainly attributed to the absence of protection systems for polluted streams that collect and recycle the untreated wastewater.

Reference Evapotranspiration Modeling Using New Heuristic Methods.

The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models.