Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

TL;DR

This study demonstrates that a multilayer perceptron neural network can effectively forecast drought conditions using SPEI data, aiding water management decisions in drought-prone regions.

Contribution

The paper introduces the application of MLPNN for drought prediction using SPEI data in Pakistan, showing its potential for early drought detection.

Findings

MLPNN achieved low MAE and RMSE in drought forecasting.

The model showed a high correlation coefficient, indicating good predictive performance.

MLPNN can assist water resource management in drought-prone areas.

Abstract

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.