Deep learning based drought assessment and prediction framework

  title={Deep learning based drought assessment and prediction framework},
  author={Amandeep Kaur and Sandeep Kumar Sood},
  journal={Ecol. Informatics},
  • A. Kaur, S. Sood
  • Published 1 May 2020
  • Computer Science, Environmental Science
  • Ecol. Informatics

Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors

This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors,

Drought Prediction and Validation for Desert Region using Machine Learning Methods

  • Azmat RajaG. T.
  • Computer Science
    International Journal of Advanced Computer Science and Applications
  • 2022
Drought prediction serves as an early warning to the effective management of water resources to avoid the drought impact and DNN is suitable to predict drought in all the four types of desert region.

Droughts across China: Drought factors, prediction and impacts.

Artificial intelligence application in drought assessment, monitoring and forecasting: a review

Drought is a natural hazard creating havoc on economic, social and environmental aspects. As a result of its slow and creeping nature, it is problematic to establish the onset as well as the

Drought prediction using hybrid soft-computing methods for semi-arid region

Drought is one of the most significant natural disaster and prediction of drought is a key aspect in effective management of water resources and reducing the effect of a drought with preliminary

Multitemporal meteorological drought forecasting using Bat-ELM

The development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling, which indicates the new model approximately 20 and 15% improves the forecasting accuracy of traditional ANN and classic ELM techniques, respectively.

Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China

A hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed that can produce more accurate predictions than the BP-ANN model and has great potential for promoting drought early warning in arid regions.

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models

Drought prediction based on an improved VMD-OS-QR-ELM model

To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition

Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling

The hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR in modeling the RDI time series of studied locations.



Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network

This paper compares the 1-, 6- and 12-month prediction of the ARIMA statistical model with LSTM using multivariate input in hopes of bettering said performance.

A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring

A model space search approach was adopted to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step, showing the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months.

Drought forecasting by ANN, ANFIS, and SVM and comparison of the models

High accuracy of these models is shown, which indicates that the SVM model gives more accurate values for forecasting than ANN, adaptive neuro-fuzzy interface system, and support vector machine.

An investigation of drought prediction using various remote-sensing vegetation indices for different time spans

ABSTRACT Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural and repeatable phenomenon definable at specified time and area. In addition, social

A prototype web-based analysis platform for drought monitoring and early warning

The currently existing drought monitor and early warning systems are reviewed, applicable remote sensing datasets and drought indicators are discussed and the development of a web-based quasi-real-time Global Drought Monitoring & Analysis Platform (Web-GDMAP) is presented.

Drought Prediction System for Improved Climate Change Mitigation

A new intelligent system concept for drought information extraction and predictions from satellite images is developed that can be developed to a full system and is helpful for extracting the freely available satellite images for drought monitoring and climate change mitigation applications at different levels of decision making.

Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia

The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six

Wireless sensor network based flood/drought forecasting system

A novel wireless sensor network (WSN) based flood/d drought forecasting system (FDFS) for Pakistan to help authorities gain early information regarding flooding/drought possibilities and take timely measures for the relief.

MapReduce functions to remote sensing distributed data processing—Global vegetation drought monitoring as example

An abstract data format is proposed to discretize the multidimensional remote sensing data for easy‐distributed storage and computation using MapReduce paradigm, and the complexity of remote sensing algorithms is resolved.