http://jgss.uob.edu.pk/journal/index.php/jgss/issue/feed 2025-08-26T10:25:12+05:00 Dr. Sanaullah Panezai dit@um.uob.edu.pk Open Journal Systems <p>The Journal of Geography and Social Sciences is an open-access, peer-reviewed, multidisciplinary journal that publishes substantive and integrative research articles in all areas of geography and social sciences. This journal is published online biannually in the months of June and December by Department of Geography and Regional Planning, University of Balochistan, Quetta. It is an open access journal, free for readers.</p> <p>We provide supportive and accessible services for our authors throughout the publishing process. benefits of publishing with JGSS are as follows;</p> <ol> <li>HEC Recognition: The Journal of Geography and Social Sciences (JGSS) intends to apply for HEC recognition after publishing its due volumes. Currently, JGSS follows the Y-category peer review process as per HEC guidelines.</li> <li>Quality: We are committed to the highest standards of blind peer review. The Editorial Board of the journal determines their peer review policy while maintaining our high standards.</li> <li>Rapid Publication: Manuscripts are single-blind peer reviewed, and the first decision takes approximately seven working days after submission. The review process is completed within 8 weeks. Acceptance to publications is undertaken in an average of two weeks.</li> <li>Open Access: All articles accepted for publication are open accessed in HTML and PDF formats.</li> <li>Publishing Ethics: JGSS adheres to Committee on Public Ethics (<a href="http://publicationethics.org/">COPE</a>).</li> <li>Visibility: We are committed to increase the visibility of articles through accessing major data bases and prominent indexing agencies.</li> <li>ISSN: The JGSS has an electronic E-ISSN: 2708-2253. </li> <li>Copy Rights under Creative Common (CC) Licence: Articles of the JGSS are licenced under Creative Commons (CC) which is a form of licensing that allows any user to “distribute, remix, tweak, and build upon your work,” provided that they credit the original authors in all cases.</li> </ol> http://jgss.uob.edu.pk/journal/index.php/jgss/article/view/36 2025-08-26T10:25:12+05:00 Sabiha Munir biyabaloch935@gmail.com Farhat Iqbal fahariqb@gmail.com

<p>Background: Drought is harmful to the environment and human life and has a major impact on reducing the quality of life. It’s a natural disaster whose negative effects spill into farms, water resources, and ecosystems, causing crop failures and food insecurity. Thus, it is one of the important global issues. Objective: In this research, we intend to model drought in different regions of Balochistan, Pakistan, using machine learning and traditional methods. The data of monthly precipitation and minimum and maximum temperature from 1951 to 2017 from five stations in Balochistan were used. Methods: The commonly used method of Autoregressive Integrated Moving Average with independent variables (ARIMAX) was compared with three machine learning methods, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The Standardized Precipitation Index (SPI) based on three, six and nine months was chosen as a drought indicator. Four different models using the lagged values of variables were developed. To evaluate the accuracy of these models, three statistical measures, RMSE, MAE, and R2 were used. Results: Based on the results of this study, we found that the RF method with the M1 model (with two lagged values of variables) provided satisfactory results of R2 at each station we studied. Additionally, the RF model showed the best results for the Panjgur station. In this study, we obtained R2 values 0.825, 0.756, 0.584, 0.731, 0.902 for Dalbandin, Quetta, Sibi, Zhob and Panjgur stations, respectively. These results were average training and testing results for each station. The RMSE, MAE and R2 values of the Panjgur station were 0.177, 0.115, 0.974, 0.337, 0.227, 0.902 during the training and testing phases, respectively. Conclusion: We found that the RF model has a high potential to forecast drought more precisely than other alternative approaches due to its great accuracy and the outcomes of this study will help the researchers accurately predict droughts.</p>

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