The rapid proliferation of the Internet of Things (IoT) technologies, underwater sensors and tracking systems, and advances in signal processing and information systems aid in the development of sensing technologies as well as software-defined networks, which enable effective water monitoring and modeling. Sensing and controlling systems capable of effectively monitoring bio-physiochemical parameters, as well as detecting concentrations of sediments and solutes are critical for investigating water health and bio-physiochemical evolutions, as well as timely implementing prevention and management strategies. The research topics covered by the special issue are:

  • Remote hydrological sensor networks in the context of citizen science,

  • Sustainable water management and cost–benefit analysis,

  • Real-time flood forecasting and warning systems,

  • Real-time, on-site and in-situ monitoring in water and marine environments,

  • Underwater acoustic signaling and interactive visualizing technologies,

  • Transportation and environmental pollution analysis on water quality,

  • Advanced signal processing techniques for underwater wireless communications,

  • MIMO and diversity systems in underwater communications,

  • Satellite remote sensing for water quality monitoring from space,

  • Irrigation water management using drones and satellites.

This special issue presents twelve peer-reviewed original research meant to introduce novel efforts to solve new challenges and problems in Sensor Technologies in Hydrological Sciences:

The paper by Jayagopal et al. (2022a) "Identifying region specific seasonal crop for leaf borne diseases by utilizing deep learning techniques" proposed an efficient way of identifying diseases specific leaves in plants through image recognition techniques in the Vellore region. It helps to improve different region-specific infestations that arise due to climatic and seasonal changes.

The paper by Malothu et al. (2022) "Tropical cyclone detection in South Pacific and Atlantic coastal areas using optical flow estimation and RESNET deep learning model" analyzed the Tropical Cyclones (TC) and meteorologists' track using satellite images. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. This paper presents an automatic TC detection algorithm and optical flow estimation to extract the features of identifying cyclones’circular patterns.

The paper by Ezhilarasi and Rekha (2022), "Improved Fuzzy-Ant colony optimization to recommend cultivation in TamilNadu, India" aimed to increase the profit of the farmers by suggesting the suitable crop recommendation in towns and villages of Tamil Nadu. Fuzzy ant clustering with detection of cluster similarity is used to provide crop recommendations to farmers depending on the current season and soil type.

In the work "Sustainable optimized LSTM based intelligent system for air quality prediction in Chennai", by Gunasekar et al. (2022), novel optimised DL algorithms are proposed for the efficient prediction of air quality particularly focussing on Chennai, Tamil Nadu region, India. To provide higher accuracy in air quality prediction, novel optimised DL algorithms are proposed which is combined several models like ARIMA and CNN-LSTM and Tuna Optimization Algorithm.

The paper by Jayagopal et al. (2022a, b) "Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning" focused on forecasting maize yield production in different climate changes in Saudi Arabia. Forecasting maize output with some lead time can help producers to prepare for requirements and, in many cases, limited human resources, as well as support strategic business decisions. The Machine learning approaches are used to identify the relationship between various climatic characteristics and maize production. The proposed methodology forecasts overall crop yield in diverse neighbourhoods in Saudi Arabia’s regions.

The paper by Gayathri Devi et al. (2022), "Radial Basis Function Neural Network and Salp Swarm Algorithm for paddy leaf diseases classification in Thanjavur, Tamilnadu Geographical Region" presented research for paddy leaf diseases classification in Thanjavur, Tamilnadu Geographical Region. This paper focuses on a novel method for detecting and identifying paddy leaf diseases at the early stages in the Thanjavur region using a radial basis function neural network (RBFNN) classifier. The proposed method utilizes the data from the TNAU agritech portal, IRRI knowledge bank, UCI machine learning repository databases, which have healthy and diseased images. This work illustrates four categories (Bacterial Blast, Bacterial Blight, Leaf Tungro and Brown Spot) of infected paddy images along with the normal set of images. Grey level co-occurrence matrix extracts the Texture features from the segmented image and the RBFNN classifier performs the disease classification and improves the detection accuracy by optimizing the data using Salp Swarm Algorithms.

The paper by Srivastava et al. (2022) "The Hydrological Impact of Tropical Cyclones on Soil Moisture using a Sensor-based Hybrid Deep Learning Model" analysed the Tropical Cyclones' effects on the Indian Ocean and their impact on soil moisture. The proposed observations-driven prediction model is designed to quantify the soil moisture variations in everyday, county-based model meteorology, and evaluate the cause of cyclones and heavy rainfall. The results are experimented with using data related to climatic and environmental supplied by the Indian Meteorological Department (IMD).

In the paper "Iceberg detection and tracking using two-level feature extraction methodology on Antarctica Ocean" Krishnan et al. (2022) developed an automatic feature extraction system for detecting icebergs in Antarctica. Establishing the appropriate spatial relationship between pixels is not producing much accuracy with the standard low-level features. To overcome this challenge the proposed method introduces the two-level iceberg detection and tracking algorithm. Based on the novel method, iceberg movement has been tracked using temporal image data. The distance moved in both temporal images is computed with the help of latitude and longitude information.

The paper by Jamain et al. (2022) "An Analysis of Innovative and Maintenance Free Active Pre-Stressed Soil Nail with Reinforced Grout Hybrid Anchor in the region of Rawang, Malaysia" introduced a cylindrical shape reinforced grout hybrid anchor in the region of Malaysia. Soil nail system (i.e., passive and active soil nail systems integrated with tendons pre-stressed) is widely applied to strengthened slopes. However, slope failures occurred observing flaws in the fundamental design. The loss of the pre-stressed tension on the creep behaviour of the nail tendon affects the functionality of the soil nail system. The proposed method behaviour of the anchor was assessed soil in the pull-out box and introduced a maintenance-free active soil nail system.

The paper by Muthukumaran et al. (2022) "Traffic Flow Prediction in Inland Waterways of Assam Region Using Uncertain Spatiotemporal Correlative Features" analysed the spatiotemporal correlation of current traffic flow in the Inland Waterways of Assam Region. Traffic flow prediction is essential for a good travel experience, but adequate computer processes for processing unpredictable spatiotemporal data in the inland water transportation industry are lacking. The proposed deep learning-based computing process, namely Convolution Neural Network-Long Short-Term Memory Network (CNN-LSTM), is a progressive predictor of employing uncertain spatiotemporal information to decrease navigation mishaps, and traffic and flow prediction failures during transportation.

The paper by Mathivanan and Jayagopal (2022) "Utilizing satellite and UAV data for crop yield prediction and monitoring through deep learning" focused on how deep learning (DL) has been used with drone technology to create solutions for detecting crop fields within a certain region of interest (ROI). Drones are used to take image samples from crop fields to examine crops and exploit data to increase productivity. A novel method is offered for detecting and tracking crop fields using a single camera on uncrewed aerial vehicles (UAV). The hybrid crop field detection model is to evaluate real UAV recordings. The findings suggest that hybrid crop field detection successfully detects and tracks crop fields through tiny UAVs with low computational resources.

The paper by Sankaran et al. (2022) "An Automated Prediction of Remote Sensing data of Queensland-Australia for Flood and Wildfire susceptibility using BISSOA-DBMLA scheme" aimed to detect the disaster occurrence from the sensed data which aids in providing a warning to the public and to safeguard them by taking necessary actions. To achieve autonomous disaster prediction, this article makes use of current developments in remote sensing, which speed up the availability of aerial/satellite data from areas of Queensland, Australia, that are more prone to disasters from an eagle-eye perspective.