# Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

arXiv: https://arxiv.org/abs/1904.11576 · HTML: https://mlsanity.com/paper/1904.11576/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM](https://mlsanity.com/paper/2107.13870/) (2021)
- [Neural Networks for Infectious Diseases Detection: Prospects and Challenges](https://mlsanity.com/paper/2112.03571/) (2021)
- [Optimizing Uterine Synchronization Analysis in Pregnancy and Labor through Window Selection and Node Optimization](https://mlsanity.com/paper/2402.14827/) (2024)

## Abstract (arXiv)

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation R, and Root Mean Square Error (RMSE). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision making.