Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Responses to Drought and Flooding

Responses to Drought and Flooding

Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.

Precipitation Processes

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

Precipitation Titration: Endpoint Detection Methods

Precipitation Titration: Endpoint Detection Methods

In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Integrated hyperspectral-multispectral mapping of shear-associated hydrothermal alteration and gold mineralization validated by geochemical and isotopic data, southern Arabian Shield.

Scientific reports·2026

Same author

Principled XAI analysis of the deep learning-based landslide susceptibility prediction model.

Scientific reports·2026

Same author

Climate change scenarios across South-Kivu agroecological zones, Eastern D.R. Congo.

Scientific reports·2026

Same author

Estimation of surface PM<sub>2.5</sub> over the Indo-Gangetic Basin using MERRA-2 reanalysis and machine learning.

Scientific reports·2026

Same author

A comprehensive approach for waste management with GAN-augmented classification.

PeerJ. Computer science·2025

Same author

Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires.

Scientific reports·2025

Same journal

Decoding seasonal urban heat dynamics at neighborhood-scale using explainable deep learning for climate-resilient, digital twin-ready green planning.

The Science of the total environment·2026

Same journal

The effects of microcystin-LR and its location within an environmental pool on rusty crayfish (Faxonius rusticus) behavior and physiology.

The Science of the total environment·2026

Same journal

An advanced hydrological approach for the characterization of the Water Scarcity Footprint at the sub-basin level.

The Science of the total environment·2026

Same journal

Irrigation management and groundwater recharge in Mediterranean intermontane basins: a multi-method evaluation of agricultural controls and interbasin variability.

The Science of the total environment·2026

Same journal

Environmental variables improve remote sensing-based water table monitoring in peatlands.

The Science of the total environment·2026

Same journal

Climate extremes, WASH deficits, and infectious diseases in the Brazilian Amazon: Insights from explainable machine learning (2010-2022).

The Science of the total environment·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 23, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Solving transparency in drought forecasting using attention models.

Abhirup Dikshit¹, Biswajeet Pradhan², Mazen E Assiri³

¹Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia.

The Science of the Total Environment

|May 13, 2022

Summary

This summary is machine-generated.

This study forecasts meteorological droughts using an attention-based deep learning model. The interpretable AI approach reveals key climatic drivers, enhancing drought management strategies.

Keywords:

Attention models Australia Data-driven models Drought forecasting Interpretable models

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Related Experiment Videos

Last Updated: Sep 23, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Climate Science
Artificial Intelligence
Hydrology

Background:

Droughts are recurring natural disasters necessitating effective management strategies.
Accurate drought forecasting relies on understanding complex hydro-meteorological and climatic factors.
Traditional neural networks struggle with non-linear variables and overfitting in drought prediction.

Purpose of the Study:

To forecast meteorological droughts using an attention-based deep learning model.
To interpret the deep neural network's forecasting process and identify key drivers.
To assess the model's performance for short-term drought prediction (1-3 months) in Eastern Australia.

Main Methods:

Utilized an attention-based deep learning model for drought forecasting.
Applied the model to meteorological drought prediction (Standard Precipitation Index).
Analyzed variable importance and temporal dependencies at different lead times.

Main Results:

The attention-based model effectively forecasts meteorological droughts at short-term lead times.
Identified the significance of large-scale climatic indices for specific study sites.
Visualized variable importance and dependencies, offering insights into model predictions.

Conclusions:

Deep learning, particularly attention-based models, offers a robust solution for drought forecasting.
Interpretable AI models enhance trust and transparency for decision-makers in drought management.
Spatio-temporal explainable AI models are crucial for advancing data-driven drought prediction.