This study was conducted to evaluate the quality of water in selected dams in Albaha region, Kingdom of Saudi Arabia. Water samples from eight dams were subjected to physical, chemical, and bacteriological assessment using standardized procedures of conductivity, total dissolved solids, ions, acidity & alkalinity, and EC blue 100® coliform detection. About three fourth (75%) of dams’ water samples exceeded the permissible levels of pH, total dissolved solids, turbidity, Mn and NO 3 set by Saudi standards. Average levels of total dissolved solids, Fe, Mn, SO 4 , NO 3 , and NO 2 were 3065.00, 0.10, 0.89, 68.25, 17.91 and 0.016 mg/L, respectively. However, the average pH of water samples was 7.95 ± 0.66 which still within the accepted range set by national and global standards. Moreover, total dissolved solids also exceeded regular standards of Food and Agriculture Organization for irrigation water quality. Coliform bacteria were detected in 37.5% of dams without any significant spatial differences between dams and sites as groups. Correlations were found between pH & NO 3 , SO 4 & NO 3 , coliform bacteria & turbidity, coliform bacteria & NO 2 levels. Increased concentrations of assessed parameters in dams may be attributed to agricultural activities as well as animal and human wastes deposited into dams via rainfalls and flash floods. Proper treatment of dams needs to be taken into account before consumption and irrigation.

No abstract

Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and economic value. This can be accomplished by using a model that can predict future water quality circumstances. In this study, we employed a sophisticated artificial neural network (ANN) model. This study intends to develop a hybrid model of single exponential smoothing (SES) with bidirectional long short-term memory (BiLSTM) and an adaptive neurofuzzy inference system (ANFIS) to predict water quality (WQ) in different groundwater in the Al-Baha region of Saudi Arabia. Single exponential smoothing (SES) was employed as a preprocessing method to adjust the weight of the dataset, and the output from SES was processed using the BiLSTM and ANFIS models for predicting water quality. The data were randomly divided into two phases, training (70%) and testing (30%). Efficiency statistics were used to evaluate the SES-BiLSTM and SES-ANFIS models’ prediction abilities. The results showed that while both the SES-BiLSTM and SES-ANFIS models performed well in predicting the water quality index (WQI), the SES-BiLSTM model performed best with accuracy (R = 99.95% and RMSE = 0.00910) at the testing phase, where the performance of the SES-ANFIS model was R = 99.95% and RMSE = 2.2941 × 100-07. The findings support the idea that the SES-BilSTM and SES-ANFIS models can be used to predict the WQI with high accuracy, which will help to enhance WQ. The results demonstrated that the SES-BiLSTM and SES-ANFIS models’ forecasts are accurate and that both seasons’ performances are consistent. Similar investigations of groundwater quality prediction for drinking purposes should benefit from the proposed SES-BiLSTM and SES-ANFIS models. Consequently, the results demonstrate that the proposed SES-BiLSTM and SES-ANFIS models are useful tools for predicting whether the groundwater in Al-Baha city is suitable for drinking and irrigation purposes.

www.fao.org/publications 6 ‫واإلطارات‬ ‫واألشكال‬ ‫اجلداول‬ ‫الباحة‬ ‫منطقة‬ ‫موقع‬ )1( ‫شكل‬ ‫للمنطقة‬ ‫الرئيسية‬ ‫التضاريس‬ )2( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫من‬ ‫مختلفة‬ ‫جوية‬ ‫رصد‬ ‫محطات‬ ‫في‬ ‫السنوية‬ ‫األمطار‬ ‫متوسط‬ )3( ‫شكل‬ ‫المنطقة‬ ‫في‬ ‫النباتي‬ ‫الغطاء‬ ‫كثافة‬ )4( ‫شكل‬ ‫السائدة‬ ‫النباتية‬ ‫الفصائل‬ ‫في‬ ‫األنواع‬ ‫لتمثيل‬ ‫المئوية‬ ‫النسبة‬ )5( ‫شكل‬ ‫الرئيسية‬ ‫النباتية‬ ‫األجناس‬ ‫في‬ ‫النباتية‬ ‫األنواع‬ ‫عدد‬ )6( ‫شكل‬ ‫المتوسط‬ ‫األبيض‬ ‫البحر‬ ‫حوض‬ ‫إقليم‬ ‫نباتات‬ ‫انتشار‬ ‫مناطق‬ )7( ‫شكل‬ ‫المنطقة‬ ‫في‬ Juniperus procera ‫العرعر‬ ‫أشجار‬ ‫مجتمع‬ ‫كثافة‬ )8( ‫شكل‬ Acacia origena ‫(الكنهلب)‬ ‫الطلح‬ ‫مجتمع‬ ‫كثافة‬ )9( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫في‬ Acacia asak ‫الضهيان‬ ‫مجتمع‬ ‫كثافة‬ )10( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫في‬ Acacia ehrenbergiana ‫السلم‬ ‫كثافة‬ )11( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫في‬ )Acacia tortilis( ‫السمر‬ ‫كثافة‬ )12( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫في‬ )Acacia etbaica(‫القرض‬ ‫كثافة‬ )13( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫في‬ ‫الشث‬ ‫كثافة‬ )14( ‫شكل‬ ‫اسة‬ ‫الدر‬ ‫منطقة‬ ‫في‬ ‫العتم‬ ‫كثافة‬ )15( ‫شكل‬ ‫السائدة‬ ‫النباتية‬ ‫الفصائل‬ )1( ‫جدول‬ 2 2 3 4 6 6 7 10 11 12 14 15 16 18 19 ‫االشكال‬ ‫اجلداول‬ ‫املالحق‬ ‫األشجار‬ ‫أنواع‬ ‫على‬ ‫للتعرف‬ ‫المستخدمة‬ ‫المفاتيح‬ ‫لبعض‬ ‫توضيحية‬ ‫رسومات‬ 1 ‫ملحق‬ ‫المنطقة‬ ‫في‬ ‫الرئيسية‬ ‫المناخية‬ ‫لألقاليم‬ ‫وفقا‬ ‫األشجار‬ ‫توزيع‬ 2 ‫ملحق‬ ‫ـتخدامات‬ ‫واسـ‬ ‫ـع‬ ‫وتوزيـ‬ ‫ـكل‬ ‫شـ‬ ‫ـى‬ ‫علـ‬ ‫ـة‬ ‫للداللـ‬ ‫ـتخدمت‬ ‫اسـ‬ ‫ـي‬ ‫التـ‬ ‫ـرة‬ ‫المختصـ‬ ‫ـوز‬ ‫الرمـ‬ ‫ـدول‬ ‫جـ‬ 3 ‫ـق‬ ‫ملحـ‬ ‫األشجار‬ ‫األشجار‬ ‫فيها‬ ‫تتواجد‬ ‫التي‬ ‫الرئيسية‬ ‫التضاريس‬ ‫على‬ ‫للداللة‬ ‫المستخدمة‬ ‫الرموز‬ 4 ‫ملحق‬ FAO, 2005( ‫طول3‬ ‫ـاد‬ ‫اعتـ‬ ‫ـم‬ ‫تـ‬ ‫ـد‬ ‫فقـ‬ ‫ـة،‬ ‫واالجتاعيـ‬ ‫ـة‬ ‫البيئيـ‬ ‫ات‬ ‫ـار‬ ‫لالعتبـ‬ ً ‫ـبة‬ ‫ونسـ‬ ‫ـة،‬ ‫اململكـ‬ ‫يف‬ ‫ـجار‬ ‫األشـ‬ ‫ـة‬ ‫ألهميـ‬ ً ‫ا‬ ‫ـر‬ ‫ونظـ‬ ‫ـس.‬ ‫األطلـ‬ ‫ـذا‬ ‫هـ‬ ‫يف‬ ‫ـجار‬ ‫األشـ‬ ‫ـف‬ ‫لتصنيـ‬ ‫أدىن‬ ‫ـد‬ ‫كحـ‬ ‫م‬ vi ‫الدراسة‬ ‫منطقة‬ ‫لها‬ ‫املجاورة‬ ‫واملواقع‬ ‫الباحة‬ ‫منطقة‬ ‫أشجار‬ ‫المناخ‬ .2.1 ،)Koppen›s, 1939( ‫ـز‬ ‫كوبنـ‬ ‫ـيم‬ ‫تقسـ‬ ‫ـب‬ ‫حسـ‬ ‫جاف‬ ‫إىل‬ ‫ـاف‬ ‫جـ‬ ‫ـبه‬ ‫شـ‬ ً ‫مناخا‬ ‫ـة‬ ‫الباحـ‬ ‫ـة‬ ‫منطقـ‬ ‫ـاخ‬ ‫منـ‬ ‫ـرب‬ ‫يعتـ‬ ‫وشــبه‬ ‫املــداري‬ ‫أو‬ ،)Ghazanfar,1998( ‫القــاري‬ ‫املــداري‬ ‫املنــاخ‬ ‫نطــاق‬ ‫ضمــن‬ ‫تصنيفــه‬ ‫تــم‬ ‫كــا‬ ‫ـر‬ ‫عسـ‬ ‫ـة‬ ‫مبنطقـ‬ ‫ـأن‬ ‫الشـ‬ ‫ـو‬ ‫هـ‬ ‫ـا‬ ‫كـ‬ ‫ـة،‬ ‫الباحـ‬ ‫ـة‬ ‫ومنطقـ‬ )Ayele and Al Shadily,2000( ‫ـاف‬ ‫الجـ‬ ‫ـداري‬ ‫املـ‬ ‫والتــي‬ ،)Indian Ocean monsoons( ‫الهنــدي‬ ‫املحيــط‬ ‫مــن‬ ‫القادمــة‬ ‫املوســمية‬ ‫للريــاح‬ ‫تخضــع‬ ‫ـر‬ ‫003ملليمـ‬ ‫ـطها‬ ‫متوسـ‬ ‫ـغ‬ ‫يبلـ‬ ‫ـار‬ ‫أمطـ‬ ‫ـقوط‬ ‫سـ‬ ‫يف‬ ‫ـببة‬ ‫متسـ‬ ‫ـارس،‬ ‫ومـ‬ ‫ـر‬ ‫أكتوبـ‬ ‫ـهري‬ ‫شـ‬ ‫ـن‬ ‫بـ‬ ‫ـادة‬ ‫عـ‬ ‫ـدث‬ ‫تحـ‬ ‫مــن‬ ‫القادمــة‬ ‫املوســمية‬ ‫الريــاح‬ ‫أن‬ ‫كــا‬ .)Robert A. Weller an d Susumu Honjo ,1997( ‫هطــول‬ ‫إجــايل‬ ‫يف‬ ‫تســاهم‬ ‫والرطوبــة‬ ‫بالبخــار‬ ‫املحملــة‬ ‫األحمــر‬ ‫البحــر‬ ‫ميــاه‬ ‫فــوق‬ ‫مــن‬ ‫الغــرب،‬ ‫ـة‬ ‫خاصـ‬ ، ً ‫ـتاء‬ ‫شـ‬ ً ‫ـرة‬ ‫ممطـ‬ ً ‫ـاردة‬ ‫بـ‬ ‫و‬ ً ‫ـا‬ ‫صيفـ‬ ‫ـة‬ ‫معتدلـ‬ ‫ـواء‬ ‫أجـ‬ ‫ـاء‬ ‫إضفـ‬ ‫ـايل‬ ‫وبالتـ‬ ‫ـة،‬ ‫الرطوبـ‬ ‫ـادة‬ ‫وزيـ‬ ‫ـار‬ ‫األمطـ‬ ‫الفصيلة‬ ‫النباتات‬ ...